Hot off the presses! Feb 01 Nat Neurosci

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Latest Articles Include:

• Focus on computational and systems neuroscience
  - Nat Neurosci 14(2):121 (2011)
  Nature Neuroscience | Editorial Computational and Systems Neuroscience Focus issue: February 2011 Volume 14, No 2 * * Reviews * Articles * Technical Report * * Contents * Editorial * Perspectives Focus on computational and systems neuroscience Journal name:Nature NeuroscienceVolume: 14,Page:121Year published:(2011)DOI:doi:10.1038/nn0211-121Published online26 January 2011 Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Neuroscience for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. We present a special focus on computational and systems neuroscience, highlighting recent advances in combining empirical and theoretical approaches, including work presented at the Cosyne meeting in past years. View full text Read the full article * Instant access to this article: US$32Buy now * Subscribe to Nature Neuroscience for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data
• You get what you get and you don't get upset
  - Nat Neurosci 14(2):123-124 (2011)
  Nature Neuroscience | News and Views You get what you get and you don't get upset * Dario L Ringach1 Contact Dario L Ringach Search for this author in: * NPG journals * PubMed * Google ScholarJournal name:Nature NeuroscienceVolume: 14,Pages:123–124Year published:(2011)DOI:doi:10.1038/nn0211-123Published online26 January 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Neuroscience for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. A study now reveals that ON and OFF thalamic inputs to visual cortex are partially segregated in space and predict the preferred orientation of neurons of the target cortical column. This finding brings us a step closer to a full understanding of the origin of simple cells and orientation maps in primary visual cortex. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Dario L. Ringach is in the Department of Neurobiology, University of California Los Angeles, Los Angeles, California, USA. Competing financial interests The author declares no competing financial interests. Corresponding author Correspondence to: * Dario L Ringach Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Neuroscience for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data
• Fighting the famine with an amine: synaptic strategies for smart search
  - Nat Neurosci 14(2):124-126 (2011)
  Nature Neuroscience | News and Views Fighting the famine with an amine: synaptic strategies for smart search * Stephan J Sigrist1 Contact Stephan J Sigrist Search for this author in: * NPG journals * PubMed * Google Scholar * Till F M Andlauer1 Contact Till F M Andlauer Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorsJournal name:Nature NeuroscienceVolume: 14,Pages:124–126Year published:(2011)DOI:doi:10.1038/nn0211-124Published online26 January 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Neuroscience for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Hunger makes Drosophila larvae move faster to find food. A new study suggests the underlying functional and structural plasticity, showing that hunger increases release of octopamine and branching of motor neurons. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Stephan J. Sigrist and Till F.M. Andlauer are in the Institute for Biology/Genetics, Freie Universität Berlin, Germany, and are members of NeuroCure Cluster of Excellence (Deutsche Forschungsgemeinschaft). Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Stephan J Sigrist or * Till F M Andlauer Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Neuroscience for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data
• Ubiquitin regulation of neuronal excitability
  - Nat Neurosci 14(2):126-128 (2011)
  Nature Neuroscience | News and Views Ubiquitin regulation of neuronal excitability * Sriharsha Kantamneni1 Search for this author in: * NPG journals * PubMed * Google Scholar * Kevin A Wilkinson1 Search for this author in: * NPG journals * PubMed * Google Scholar * Jeremy M Henley1 Contact Jeremy M Henley Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:126–128Year published:(2011)DOI:doi:10.1038/nn0211-126Published online26 January 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Neuroscience for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Demonstrating the common mechanism of proteasome-dependent degradation of ion channels, two studies in this issue of Nature Neuroscience show that ubiquitin-dependent protein degradation can modulate neuronal excitability. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Sriharsha Kantamneni, Kevin A. Wilkinson and Jeremy M. Henley are at the Medical Research Council Centre for Synaptic Plasticity, School of Biochemistry, Medical Sciences Building, University of Bristol, Bristol, UK. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Jeremy M Henley Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Neuroscience for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data
• Integration and autonomy in axons
  - Nat Neurosci 14(2):128-130 (2011)
  Nature Neuroscience | News and Views Integration and autonomy in axons * Barry W Connors1 Contact Barry W Connors Search for this author in: * NPG journals * PubMed * Google Scholar * Omar J Ahmed1 Contact Omar J Ahmed Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorsJournal name:Nature NeuroscienceVolume: 14,Pages:128–130Year published:(2011)DOI:doi:10.1038/nn0211-128Published online26 January 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Neuroscience for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. A new study shows that even under normal conditions repetitive spiking in some cortical interneurons can trigger spontaneous spiking that originates from distal axons and lasts for tens of seconds. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Barry W. Connors and Omar J. Ahmed are in the Department of Neuroscience, Brown University, Providence, Rhode Island, USA. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Barry W Connors or * Omar J Ahmed Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Neuroscience for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data
• Attention points to the future
  - Nat Neurosci 14(2):130-131 (2011)
  Nature Neuroscience | News and Views Attention points to the future * Richard J Krauzlis1 Contact Richard J Krauzlis Search for this author in: * NPG journals * PubMed * Google Scholar * Samuel U Nummela1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:130–131Year published:(2011)DOI:doi:10.1038/nn0211-130Published online26 January 2011 Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Neuroscience for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Our perception of the visual world is stable despite saccade-caused retinal input shifts. A new behavioral study shows that this stability may be achieved by predictively remapping attention before eye movements begin. View full text Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Affiliations * Richard J. Krauzlis and Samuel U. Nummela are in the Systems Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, California, USA. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Richard J Krauzlis Read the full article * Instant access to this article: US$18Buy now * Subscribe to Nature Neuroscience for full access: SubscribeLogin for existing subscribers Additional access options: * Use a document delivery service * Login via Athens * Purchase a site license * Institutional access * British Library Document Supply Centre * Infotrieve * Thompson ISI Document Delivery * You can also request this document from your local library through inter-library loan services. Additional data
• Multiple models to capture the variability in biological neurons and networks
  - Nat Neurosci 14(2):133-138 (2011)
  Nature Neuroscience | Perspective Computational and Systems Neuroscience Focus issue: February 2011 Volume 14, No 2 * * Reviews * Articles * Technical Report * * Contents * Editorial * Perspectives Multiple models to capture the variability in biological neurons and networks * Eve Marder1, 2 Contact Eve Marder Search for this author in: * NPG journals * PubMed * Google Scholar * Adam L Taylor1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:133–138Year published:(2011)DOI:doi:10.1038/nn.2735Published online26 January 2011 Abstract * Abstract * Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg How tightly tuned are the synaptic and intrinsic properties that give rise to neuron and circuit function? Experimental work shows that these properties vary considerably across identified neurons in different animals. Given this variability in experimental data, this review describes some of the complications of building computational models to aid in understanding how system dynamics arise from the interaction of system components. We argue that instead of trying to build a single model that captures the generic behavior of a neuron or circuit, it is beneficial to construct a population of models that captures the behavior of the population that provided the experimental data. Studying a population of models with different underlying structure and similar behaviors provides opportunities to discover unsuspected compensatory mechanisms that contribute to neuron and network function. View full text Figures at a glance * Figure 1: The pyloric rhythm has a variable period but phase relationships are held invariant. () Extracellular recordings from a slow pyloric rhythm showing its characteristic repeating pattern of PD, LP and PY neuron activity (on the LP and PY traces, only the largest spikes correspond to spikes from the LP and PY neurons respectively). Arrows indicate measurements made on each pyloric cycle. Gray arrow indicates pyloric period, measured as the latency from the onset of one PD neuron burst to the next. Colored arrows indicate latencies measured from the onset of the PD neuron burst. The dark blue arrow indicates the latency of PD neuron offset. The red arrow indicates the latency of LP neuron onset. The light blue arrow indicates the latency of LP neuron offset. The purple arrow indicates the latency of PY neuron onset. The pink arrow indicates the latency of PY neuron offset. These latencies were then divided by the period to give the phase relationships shown in . () Extracellular recordings from a fast pyloric rhythm. Data are presented as in . () Phase of burst ! onset/offset versus pyloric period. Each point represents one of 99 animals. Period is a mean period calculated over many cycles, as are phases. Dark blue points, phase of PD neuron offset; red points, phase of LP neuron onset; light blue points, phase of LP neuron offset; purple points, phase of PY neuron onset; pink points, phase of PY neuron offset. The histograms on top of the plot show the distribution of pyloric rhythm periods. The histograms on the right show the distributions of each of the phases, coded in color as for the data points. Adapted from ref. 9. * Figure 2: Example distributions of neuron parameters for neurons that all share a common behavior or set of behaviors. In all panels, dark blue dots represent individual neurons, the red cross represents the mean of the distribution and the light blue triangle represents the hypothetical neuron with all parameters set to their largest, or 'best', values. () A population with statistically independent parameters. () A population in which the mean is not representative. () A population with a strong positive correlation between parameters. () A population with a strong negative correlation between parameters. () A population with two very different subpopulations. () A population with a donut-shaped distribution. * Figure 3: Model LP neurons with similar behavior but substantially different parameters. (,) Traces from two randomly generated model LP neurons receiving ongoing pyloric-like synaptic input. () The parameters for the two models, which are quite different. Red and blue bars show the parameters of the model that generated the red/blue trace in . For each parameter, a red and blue bar are superimposed, with their region of overlap shown as purple. Parameters are sorted by the absolute difference between them in the two models. g– parameters are maximal conductances of different currents, E parameters are reversal potentials, P–Ca is the maximal permeability of the Ca2+ current and V½,pr is the half-activation voltage of a modulatory inward current. Max, maximum; min, minimum. The model is described in ref. 27. * Figure 4: Tolerance and degeneracy. () Plot of the map between maximal (max) conductance and firing rate for a hypothetical neuron with only a single variable conductance. Tolerance in the spike rate translates into tolerance in the maximal conductance, with the maximal conductance tolerance determined by the slope of the line. () As in , but here the relationship between spike rate and maximal conductance has a lower slope, leading to a larger tolerance in the maximal conductance for the same spike-rate tolerance. () As in and , but with a slope of zero. In this case, the firing rate is completely insensitive to the maximal conductance, and thus the maximal conductance can take on any value. () Contour plot of the map between maximal conductances and spike rate for a hypothetical neuron with two variable maximal conductances. Each line denotes the set of maximal conductances that yield the given spike rate. Although each conductance affects the spike rate, there are many combinations of maximal conductances t! hat yield the same spike rate. * Figure 5: Quantification of the effect of each model parameter on each model property for a population of LP models. The area of each circle represents the average amount of variance explained by that parameter as a fraction of the variance explained by the complete fit. The areas of the circles in each row sum to 1. Most properties show contributions from many conductances. Parameters are the same as those in Figure 3. Adapted from ref. 27. Author information * Abstract * Author information Affiliations * Biology Department, Brandeis University, Waltham, Massachusetts, USA. * Eve Marder & * Adam L Taylor * Volen Center, Brandeis University, Waltham, Massachusetts, USA. * Eve Marder & * Adam L Taylor Contributions E.M. and A.L.T. wrote and edited the paper. A.L.T. made the figures, some of which are adapted versions of figures originally published elsewhere. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Eve Marder Additional data
• How advances in neural recording affect data analysis
  - Nat Neurosci 14(2):139-142 (2011)
  Nature Neuroscience | Perspective Computational and Systems Neuroscience Focus issue: February 2011 Volume 14, No 2 * * Reviews * Articles * Technical Report * * Contents * Editorial * Perspectives How advances in neural recording affect data analysis * Ian H Stevenson1 Contact Ian H Stevenson Search for this author in: * NPG journals * PubMed * Google Scholar * Konrad P Kording1, 2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:139–142Year published:(2011)DOI:doi:10.1038/nn.2731Published online26 January 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Over the last five decades, progress in neural recording techniques has allowed the number of simultaneously recorded neurons to double approximately every 7 years, mimicking Moore's law. Such exponential growth motivates us to ask how data analysis techniques are affected by progressively larger numbers of recorded neurons. Traditionally, neurons are analyzed independently on the basis of their tuning to stimuli or movement. Although tuning curve approaches are unaffected by growing numbers of simultaneously recorded neurons, newly developed techniques that analyze interactions between neurons become more accurate and more complex as the number of recorded neurons increases. Emerging data analysis techniques should consider both the computational costs and the potential for more accurate models associated with this exponential growth of the number of recorded neurons. View full text Figures at a glance * Figure 1: Exponential growth in the number of recorded neurons. () Examining 56 studies published over the last five decades, we found that the number of simultaneously recorded neurons doubled approximately every 7 years. () A timeline of recording technologies during this period shows the development from single-electrode recordings to multi-electrode arrays and in vivo imaging techniques. Images of recording techniques reprinted from refs. 40,41,42,43 with permission of Elsevier, Springer Science + Business Media, and Am. Physiol. Soc. Image of Utah array reprinted from ref. 42, © 1999 IEEE. Ca2+ imaging reprinted from ref. 33, © 2003 Natl. Acad. Sci. USA. * Figure 2: Approaches to neural data analysis and the scaling of spike prediction accuracy. () There are two main approaches to modeling multi-electrode data: mapping tuning properties to describe how neurons relate to stimuli or movement and mapping interactions between neurons. These techniques aim to predict spiking based on either external variables or other neural signals. () In data recorded from motor cortex (top) and visual cortex (bottom), spike prediction accuracy grows when modeling interactions between neurons, but is constant when modeling tuning curves. Shaded regions denote ± s.e.m. across neurons. () An alternative approach is to consider simultaneously recorded neural activity as an expression of a latent, low-dimensional state space. These spaces can be extracted by first estimating smooth firing rates for each neuron and then using a dimensionality reduction technique such as factor analysis. Features of these state spaces can then be used to predict reaction times or reach targets on a trial-by-trial basis or to describe neural variability. Pur! ple and green ellipses represent neural variability at target onset and movement onset, respectively. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Physical Medicine and Rehabilitation, Northwestern University and Rehabilitation Institute of Chicago, Chicago, Illinois, USA. * Ian H Stevenson & * Konrad P Kording * Department of Physiology, Northwestern University, Chicago, Illinois, USA. * Konrad P Kording * Department of Applied Mathematics, Northwestern University, Chicago, Illinois, USA. * Konrad P Kording Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Ian H Stevenson Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (300K) Supplementary Table 1 and Supplementary Methods Additional data
• A proposed common neural mechanism for categorization and perceptual decisions
  - Nat Neurosci 14(2):143-146 (2011)
  Nature Neuroscience | Perspective Computational and Systems Neuroscience Focus issue: February 2011 Volume 14, No 2 * * Reviews * Articles * Technical Report * * Contents * Editorial * Perspectives A proposed common neural mechanism for categorization and perceptual decisions * David J Freedman1 Contact David J Freedman Search for this author in: * NPG journals * PubMed * Google Scholar * John A Assad2, 3 Contact John A Assad Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorsJournal name:Nature NeuroscienceVolume: 14,Pages:143–146Year published:(2011)DOI:doi:10.1038/nn.2740Published online26 January 2011 Abstract * Abstract * Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg One of the most fascinating issues in neuroscience is how the brain makes decisions. Recent evidence points to the parietal cortex as an important locus for certain kinds of decisions. Because parietal neurons are also involved in movements, it has been proposed that decisions are encoded in an intentional, action-based framework based on the movements used to report decisions. An alternative or complementary view is that decisions represent more abstract information not linked to movements per se. Parallel experiments on categorization suggest that parietal neurons can indeed represent abstract categorical outcomes that are not linked to movements. This could provide a unified or complementary view of how the brain decides and categorizes. View full text Author information * Abstract * Author information Affiliations * Department of Neurobiology, The University of Chicago, Chicago, Illinois, USA. * David J Freedman * Department of Neurobiology, Harvard Medical School, Boston, Massachusetts, USA. * John A Assad * Center for Neuroscience and Cognitive Systems, Italian Institute of Technology, Rovereto, Italy. * John A Assad Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * David J Freedman or * John A Assad Additional data
• Hippocampal replay in the awake state: a potential substrate for memory consolidation and retrieval
  - Nat Neurosci 14(2):147-153 (2011)
  Nature Neuroscience | Review Computational and Systems Neuroscience Focus issue: February 2011 Volume 14, No 2 * * Reviews * Articles * Technical Report * * Contents * Editorial * Perspectives Hippocampal replay in the awake state: a potential substrate for memory consolidation and retrieval * Margaret F Carr1, 2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Shantanu P Jadhav1, 2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Loren M Frank1, 2 Contact Loren M Frank Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:147–153Year published:(2011)DOI:doi:10.1038/nn.2732Published online26 January 2011 Abstract * Abstract * Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The hippocampus is required for the encoding, consolidation and retrieval of event memories. Although the neural mechanisms that underlie these processes are only partially understood, a series of recent papers point to awake memory replay as a potential contributor to both consolidation and retrieval. Replay is the sequential reactivation of hippocampal place cells that represent previously experienced behavioral trajectories and occurs frequently in the awake state, particularly during periods of relative immobility. Awake replay may reflect trajectories through either the current environment or previously visited environments that are spatially remote. The repetition of learned sequences on a compressed time scale is well suited to promote memory consolidation in distributed circuits beyond the hippocampus, suggesting that consolidation occurs in both the awake and sleeping animal. Moreover, sensory information can influence the content of awake replay, suggesting a role ! for awake replay in memory retrieval. View full text Figures at a glance * Figure 1: Place cell sequences experienced during behavior are replayed in both the forward and reverse direction during awake SWRs. Spike trains for 13 neurons with place fields on the track are shown before, during and after a single traversal. Sequences that occur during running (center) are reactivated during awake SWRs. Forward replay (left inset, red box) occurs before traversal of the environment and reverse replay (right inset, blue box) after. The CA1 local field potential is shown on top and the animal's velocity is shown below. Adapted with permission from ref. 47. * Figure 2: Awake replay reinstates representations of both current and past experiences. () The animal's physical location during a local replay event. () Sequential spiking of neurons with place fields in the current environment during an SWR. At top is the filtered CA1 local field potential. The color bar shows the colors associated with each 15-ms time bin used for decoding. () Probability distribution of decoded location for each time bin. Each color corresponds to the spiking in the associated time bin; gray indicates time bins where no spikes occurred. () A diagram of the local replay event, emanating away from the animal's current location. () The animal's physical location during a remote replay event. () Spiking of cells during the SWR for cells with place fields in either the remote (top) or local (bottom) environment. (,) Decoded locations reflect a coherent trajectory through the remote environment, but do not represent a coherent trajectory through the local environment (, bottom). Adapted with permission from ref. 31. * Figure 3: Spatial inputs could lead to retrieval of either local or remote sequences. Schematic illustrating how current sensory input could trigger either local or remote replay. Current sensory information relating to the animal's current location activates cells with place fields nearby. These cells act as "initiator" cells and lead to sequential reactivation of previously stored sequences. Because the initiator cell also has a place field in a spatially remote environment, this cell can initiate replay of either environment. The initiator cell leads to reactivation of stored sequences (top) through either the current environment (local replay, bottom left) or a previously experienced environment (remote replay, bottom right). The geometry of the neural ensemble is for illustration only and does not represent any actual topography of representations in the hippocampus. Author information * Abstract * Author information Primary authors * These authors contributed equally to this work. * Margaret F Carr & * Shantanu P Jadhav Affiliations * Department of Physiology, University of California, San Francisco, San Francisco, California, USA. * Margaret F Carr, * Shantanu P Jadhav & * Loren M Frank * W.M. Keck Center for Integrative Neuroscience, University of California, San Francisco, San Francisco, California, USA. * Margaret F Carr, * Shantanu P Jadhav & * Loren M Frank Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Loren M Frank Additional data
• From reinforcement learning models to psychiatric and neurological disorders
  - Nat Neurosci 14(2):154-162 (2011)
  Nature Neuroscience | Review Computational and Systems Neuroscience Focus issue: February 2011 Volume 14, No 2 * * Reviews * Articles * Technical Report * * Contents * Editorial * Perspectives From reinforcement learning models to psychiatric and neurological disorders * Tiago V Maia1, 2 Contact Tiago V Maia Search for this author in: * NPG journals * PubMed * Google Scholar * Michael J Frank3, 4 Contact Michael J Frank Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Corresponding authorsJournal name:Nature NeuroscienceVolume: 14,Pages:154–162Year published:(2011)DOI:doi:10.1038/nn.2723Published online26 January 2011 Abstract * Abstract * Author information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Over the last decade and a half, reinforcement learning models have fostered an increasingly sophisticated understanding of the functions of dopamine and cortico-basal ganglia-thalamo-cortical (CBGTC) circuits. More recently, these models, and the insights that they afford, have started to be used to understand important aspects of several psychiatric and neurological disorders that involve disturbances of the dopaminergic system and CBGTC circuits. We review this approach and its existing and potential applications to Parkinson's disease, Tourette's syndrome, attention-deficit/hyperactivity disorder, addiction, schizophrenia and preclinical animal models used to screen new antipsychotic drugs. The approach's proven explanatory and predictive power bodes well for the continued growth of computational psychiatry and computational neurology. View full text Figures at a glance * Figure 1: Principles of computational psychiatry and computational neurology. () The starting point in computational psychiatry and computational neurology is a model of normal function that captures key aspects of behavior, neural activity or both. Models at various levels of abstraction can be useful (for example, algorithmic models from machine learning or neural models from computational cognitive neuroscience). Several approaches can then be pursued. () With detailed neural models, pathophysiological processes can be simulated by making principled changes to the model that mimic biological alterations in the disorder under consideration (for example, alterations in striatal dopaminergic innervation). The systems-level and behavioral implications of these changes can then be explored, leading to testable predictions. We call this approach 'deductive' because the models are used to recreate the mechanistic link between causes (the biological abnormalities) and their consequences (abnormalities in systems-level neural activity and behavior). () A se! cond approach involves using a model to try to infer the causes of the observed abnormalities in neural activity or behavior. We call this approach 'abductive' because it involves reasoning from consequences (the behavior or systems-level neural activity) to their possible causes (the underlying biological abnormalities). In this approach, alternative a priori hypotheses concerning possible biological abnormalities in a given disorder can be compared to determine which, if any, produce the same abnormalities in behavior and neural activity that are found in the disorder (T.V.M. and B.S. Peterson, unpublished). () A third approach, used more often with algorithmic than with neural models (largely because the former tend to have fewer parameters), involves fitting the model's parameters to the behavior of individual subjects on a suitable task or set of tasks and then determining if there are parameter differences between diseased and healthy subject groups or correlations be! tween parameters and disease severity. We call this approach '! quantitative abductive' because it also involves reasoning from behavior to its mechanistic causes. A fourth approach (not shown) also involves fitting a model to subjects' behavior, but the goal is to estimate, on a trial-by-trial basis, each subject's putative internal representation of the quantities embedded in the model (for example, state values or prediction errors). These predicted internal representations are then used as regressors in functional imaging (for example, functional magnetic resonance imaging, electroencephalography), to find their neural correlates, which are then compared across the diseased and healthy groups. Each of these four approaches can also be adapted to study the effects of treatments (for example, medication or neurosurgery). Furthermore, additional leverage can sometimes be gained by the synergistic use of different approaches or models at different levels of abstraction. Behav. expt(s)., behavioral experiment(s). * Figure 2: Anatomy and modeling of CBGTC loops. () Anatomy. Striatal medium spiny neurons in the direct pathway (Go neurons) express mostly D1 receptors40 and project directly to the globus pallidus internal segment and the substantia nigra pars reticulata (GPi/SNr). Go neurons inhibit the GPi/SNr, which in turn results in disinhibition of the thalamus, thereby facilitating execution of the corresponding action. Striatal medium spiny neurons in the indirect pathway (NoGo neurons) express mostly D2 receptors40 and project to the globus pallidus external segment (GPe), which in turn projects to the GPi/SNr. NoGo neurons produce a focused removal of the tonic inhibition of the GPe on the GPi/SNr, thereby disinhibiting the GPi/SNr, which in turn results in suppression of the corresponding action in the thalamus. Neurons in the subthalamic nucleus (STN) receive direct projections from the cortex in the hyperdirect pathway and project to both the GPe and GPi/SNr. The projections from the STN to the GPe and GPi/SNr are diffuse26! , so they are believed to modulate all actions rather than a specific action. () The basal ganglia Go/NoGo model28, 35. The connections in the model are consistent with the anatomical connections shown in . The model learns to map inputs, representing the current state, to actions in the pre-supplementary motor area (preSMA) (or the SMA). Corticocortical projections from the input layer to preSMA activate in preSMA candidate actions appropriate for the current state. The basal ganglia then facilitate (gate) the best action, that is, the action with the best reinforcement history for the current state, while simultaneously suppressing the other actions (at the level of the thalamus). Distributed populations of Go and NoGo units represent the positive and negative evidence, respectively, for the candidate actions in the current state. Lateral inhibition between the Go and NoGo pathways ensures that the probability of selecting a given action is a function of the difference be! tween the positive and negative evidence for that action. The ! positive and negative evidence for each action in each state is learned on the basis of past reinforcement history, through the actions of dopamine on D1 and D2 receptors in striatal Go and NoGo units, respectively. The weights from the input layer to preSMA are themselves learned, but through Hebbian mechanisms, thereby allowing these corticocortical projections to activate candidate actions in preSMA in proportion to their prior probability of being executed in the given state. The STN prevents facilitation of suboptimal responses in high-conflict situations35. * Figure 3: The probabilistic selection task58. The probabilistic selection task assesses whether participants learn better from positive or negative outcomes. During training, in each trial, participants are presented with one of the pairs shown on top (AB, CD and EF) and select one of the two stimuli. Feedback then indicates if the choice was correct or incorrect. The probabilities of each stimulus leading to correct feedback are indicated in the figure. Participants may learn to perform accurately during training (that is, learn to select A, C and E) by learning which stimulus in each pair is associated with positive feedback (Go learning), by learning which stimulus in each pair is associated with negative feedback (NoGo learning) or both. The test phase assesses the degree to which participants learned better from positive or from negative feedback. Participants are presented with novel pairs of stimuli consisting of either an A or a B paired with each of the other stimuli (C through F, which on average had a 50% pro! bability of positive feedback during training). No feedback is provided during testing. If participants perform better on the pairs that include A than on those that include B, that indicates that they learned better to select the most positive stimulus (A) than to avoid the most negative stimulus (B), so they learn better from positive feedback (Go learning). If they perform better on the pairs that include B, they learn better from negative feedback (NoGo learning). The test phase can also be used to assess how participants adjust their behavior as a function of conflict (for example, whether they slow down to improve accuracy in high-conflict situations, such as when deciding between stimuli A and C, which have very similar reinforcement histories). Author information * Abstract * Author information Affiliations * Department of Psychiatry, Columbia University, New York, New York, USA. * Tiago V Maia * New York State Psychiatric Institute, New York, New York, USA. * Tiago V Maia * Departments of Cognitive, Linguistic and Psychological Sciences, and Psychiatry and Human Behavior, Brown University, Providence, Rhode Island, USA. * Michael J Frank * Brown Institute for Brain Science, Brown University, Providence, Rhode Island, USA. * Michael J Frank Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Tiago V Maia or * Michael J Frank Additional data
• Amygdala volume and social network size in humans
  - Nat Neurosci 14(2):163-164 (2011)
  Nature Neuroscience | Brief Communication Amygdala volume and social network size in humans * Kevin C Bickart1 Search for this author in: * NPG journals * PubMed * Google Scholar * Christopher I Wright2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Rebecca J Dautoff2, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Bradford C Dickerson2, 3, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Lisa Feldman Barrett2, 3, 5 Contact Lisa Feldman Barrett Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:163–164Year published:(2011)DOI:doi:10.1038/nn.2724Received06 October 2010Accepted24 November 2010Published online26 December 2010 Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg We found that amygdala volume correlates with the size and complexity of social networks in adult humans. An exploratory analysis of subcortical structures did not find strong evidence for similar relationships with any other structure, but there were associations between social network variables and cortical thickness in three cortical areas, two of them with amygdala connectivity. These findings indicate that the amygdala is important in social behavior. View full text Author information * Author information * Supplementary information Affiliations * Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, Massachusetts, USA. * Kevin C Bickart * Psychiatric Neuroimaging Research Program, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA. * Christopher I Wright, * Rebecca J Dautoff, * Bradford C Dickerson & * Lisa Feldman Barrett * Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts, USA. * Christopher I Wright, * Rebecca J Dautoff, * Bradford C Dickerson & * Lisa Feldman Barrett * Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA. * Bradford C Dickerson * Department of Psychology, Northeastern University, Boston, Massachusetts, USA. * Lisa Feldman Barrett Contributions C.I.W. and L.F.B. designed the study. R.J.D. and L.F.B. performed the research. K.C.B., R.J.D., B.C.D. and L.F.B. analyzed the data. K.C.B., B.C.D., C.I.W. and L.F.B. wrote the manuscript. B.C.D., C.I.W. and L.F.B. contributed to grant funding. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Lisa Feldman Barrett Supplementary information * Author information * Supplementary information PDF files * Supplementary Text and Figures (348K) Supplementary Figure 1, Supplementary Tables 1–3, Supplementary Methods, Supplementary Results and Supplementary Discussion Additional data
• UNC-6 and UNC-40 promote dendritic growth through PAR-4 in Caenorhabditis elegans neurons
  - Nat Neurosci 14(2):165-172 (2011)
  Nature Neuroscience | Article UNC-6 and UNC-40 promote dendritic growth through PAR-4 in Caenorhabditis elegans neurons * Hannah M Teichmann1 Search for this author in: * NPG journals * PubMed * Google Scholar * Kang Shen1 Contact Kang Shen Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:165–172Year published:(2011)DOI:doi:10.1038/nn.2717Received12 October 2010Accepted09 November 2010Published online26 December 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Axons navigating through the developing nervous system are instructed by external attractive and repulsive cues. Emerging evidence suggests the same cues control dendrite development, but it is not understood how they differentially instruct axons and dendrites. We studied a C. elegans motor neuron whose axon and dendrite adopt different trajectories and lengths. We found that the guidance cue UNC-6 (Netrin) is required for both axon and dendrite development. Its repulsive receptor UNC-5 repelled the axon from the ventral cell body, whereas the attractive receptor UNC-40 (DCC) was dendritically enriched and promotes antero-posterior dendritic growth. Although the endogenous ventrally secreted UNC-6 instructs axon guidance, dorsal or even membrane-tethered UNC-6 could support dendrite development. Unexpectedly, the serine-threonine kinase PAR-4 (LKB1) was selectively required for the activity of the UNC-40 pathway in dendrite outgrowth. These data suggest that axon and dendri! te of one neuron interpret common environmental cues with different receptors and downstream signaling pathways. View full text Figures at a glance * Figure 1: Development of the DA9 motor neuron in wild-type C. elegans. () Schematic diagram of DA9. The DA9 cell body is located in the tail. Its axon extends posteriorly in the ventral cord, dorsally through a commissure and anteriorly in the dorsal cord. The ventrally located soma elaborates a single, unbranched dendrite anteriorly. Gut autofluorescence, within gray patch, should be disregarded in all images. Asterisk, cell body; arrowhead, tip of dendrite. (–) Lateral view of wild-type worms expressing Pmig-13mig-13gfp in DA9, at developmental stages L1–L4 and young adult (YA). () An L1 worm. Worms hatch with a fully formed axon, while the dendrite is a stump. The dendrite orientation is already determined by L1. (–) The dendrite continues to grow anteriorly throughout the life of the worm (images L2–YA). () Growth curve of dendrite over time. Early larval dendritic growth takes place at a steady pace (L1–L3), followed by a growth spurt between L4 and YA and slowing in older adults. All pictures are lateral view with ventral down a! nd anterior to the left; all quantification from anterior edge of soma to anterior tip of dendrite. Scale bar, 20 μm. n ≥ 20. Error bars, s.e.m. * Figure 2: UNC-6 (Netrin) regulates dendrite outgrowth through UNC-40 (DCC). (–) Micrographs of wild-type and unc-6, unc-40 and unc-5 mutants expressing Pmig-13mig-13gfp, revealing truncated dendrites in unc-6 and unc-40 mutants. () Growth curves showing dendrite length in L1–L4 wild-type and unc-6 signaling mutants. A minimal defect in dendrite growth in L1 worms increases over larval growth, with a trend toward unc-40 signaling mutant phenotypes being more severe than unc-5. () Dendrite lengths in L3 worms. All pictures are lateral views with ventral down and anterior to the left, all quantification from anterior edge of soma to anterior tip of dendrite. Scale bar, 20 μm. n ≥ 20. Error bars, s.e.m. ***P < 0.001; NS, not significant; Student's t-test. * Figure 3: UNC-40 is necessary and sufficient for dendrite outgrowth. Graph of dendrite lengths in wild-type and with UNC-40 transgenes in various mutant background. All quantification is from anterior edge of soma to anterior tip of dendrite. n ≥ 20. Error bars, s.e.m. ***P < 0.001; NS, not significant; Student's t-test. * Figure 4: Local, non-graded UNC-6 signaling is sufficient for dendrite outgrowth. () Graph of dendrite length in different genetic backgrounds. (–) Schematics of the corresponding UNC-6 expression pattern (orange shading) in each background. Dendrite lengths in wild-type () and unc-6 worms (), as well as rescue of outgrowth by dorsal muscle unc-6 expression (unc-6(ev400); Ex[Punc-129unc-6]) () and membrane-tethered unc-6 expression (unc-6(ev400); Ex[Punc-6unc-6nlg-1TMmCherry]). The soma of the neighboring VA12 motor neuron (red) is marked by + (). Overexpression of membrane-tethered unc-6 is sufficient for dendrite overshooting (). All quantification from anterior edge of soma to anterior tip of dendrite. n ≥ 25. Error bars, s.e.m. ***P < 0.001; NS, not significant; Student's t-test. * Figure 5: PAR-4 (LKB1) is necessary and sufficient for dendrite outgrowth. (,) Lateral view of wild-type and par-4 mutants expressing Pmig-13mig-13gfp at L3 stage. () Graph of dendrite length in L3 wild-type, par-4, par-4 rescue (par-4(it47); Ex[Pitr-1par-4gfp]) and par-4 overexpression (Ex[Pitr-1par-4gfp]). All worms are L3 stage, all pictures are lateral view with ventral down and anterior to the left, all quantification from anterior edge of soma to anterior tip of dendrite. Scale bar, 20 μm. n ≥ 30. Error bars, s.e.m. ***P < 0.001; NS, not significant; Student's t-test. * Figure 6: PAR-4 acts downstream of UNC-6 and UNC-40. () Graph of dendrite length in L3 worms in par-4, unc-6, unc-40, unc-34 and unc-115 mutants and their double mutants with par-4. The par-4; unc-6, the unc-40; par-4 and the par-4; unc-115 mutants do not enhance, whereas unc-34; par-4 mutants show a marked enhancement in dendrite truncation. () Dendrite length in L3 worms expressing a par-4 cDNA construct in unc-6 and unc-40 mutants (unc-6(ev400); Ex[Pitr-1par-4gfp] and unc-40(e271); Ex[Pitr-1par-4gfp] and worms expressing unc-40 cDNA in a par-4 mutant (par-4(it47); Ex[Pmig-13unc-40]). Overexpression of par-4 can compensate for unc-6 or unc-40 loss, whereas unc-40 overexpression does not rescue par-4. () Graph of dendrite length in L3 worms. par-4; unc-115 double mutants do not enhance compared to single mutants, and the overextension caused by Ex[Pitr-1par-4gfp] expression is suppressed in unc-115 mutants, suggesting that unc-115 acts downstream of par-4. All quantification is from anterior edge of soma to anterior tip of de! ndrite. n ≥ 20. Error bars, s.e.m. ***P < 0.001; NS, not significant; Student's t-test. * Figure 7: unc-34 and par-4 act in parallel in AVM development. () The AVM soma is located laterally in the worm and extends a process which first migrates ventrally, then anteriorly into the head (ventral down, anterior to the left). () unc-34 and unc-40 mutants show a guidance defect in which the AVM process fails to make a ventral turn before extending12. () Graph of percentage of guidance defects in AVM. Wild-type and par-4 single mutants do not show ventral guidance defects, whereas unc-34 mutants show a low-penetrance defect. The frequency is enhanced in unc-34; par-4 double mutants. () Graph of percentage of outgrowth defects in the AVM process. Wild-type, par-4 and unc-34 single mutants consistently have AVM processes that grow beyond the pharynx terminal bulb, whereas par-4, unc-34 double mutants show a low-penetrance outgrowth defect. n ≥ 100. Error bars, standard error of proportion; **P < 0.01, *P < 0.05, χ2 test. Author information * Abstract * Author information * Supplementary information Affiliations * Howard Hughes Medical Institute, Department of Biology, Stanford University, California, USA. * Hannah M Teichmann & * Kang Shen Contributions H.M.T. performed all experiments. H.M.T. and K.S. designed and analyzed the experiments and wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Kang Shen Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1–7 and Supplementary Table 1 Additional data
• The Cavβ subunit prevents RFP2-mediated ubiquitination and proteasomal degradation of L-type channels
  - Nat Neurosci 14(2):173-180 (2011)
  Nature Neuroscience | Article The Cavβ subunit prevents RFP2-mediated ubiquitination and proteasomal degradation of L-type channels * Christophe Altier1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Agustin Garcia-Caballero1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Brett Simms1 Search for this author in: * NPG journals * PubMed * Google Scholar * Haitao You1 Search for this author in: * NPG journals * PubMed * Google Scholar * Lina Chen1 Search for this author in: * NPG journals * PubMed * Google Scholar * Jan Walcher1 Search for this author in: * NPG journals * PubMed * Google Scholar * H William Tedford1 Search for this author in: * NPG journals * PubMed * Google Scholar * Tamara Hermosilla1 Search for this author in: * NPG journals * PubMed * Google Scholar * Gerald W Zamponi1 Contact Gerald W Zamponi Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:173–180Year published:(2011)DOI:doi:10.1038/nn.2712Received10 August 2010Accepted12 November 2010Published online26 December 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg It is well established that the auxiliary Cavβ subunit regulates calcium channel density in the plasma membrane, but the cellular mechanism by which this occurs has remained unclear. We found that the Cavβ subunit increased membrane expression of Cav1.2 channels by preventing the entry of the channels into the endoplasmic reticulum–associated protein degradation (ERAD) complex. Without Cavβ, Cav1.2 channels underwent robust ubiquitination by the RFP2 ubiquitin ligase and interacted with the ERAD complex proteins derlin-1 and p97, culminating in targeting of the channels to the proteasome for degradation. On treatment with the proteasomal inhibitor MG132, Cavβ-free channels were rescued from degradation and trafficked to the plasma membrane. The coexpression of Cavβ interfered with ubiquitination and targeting of the channel to the ERAD complex, thereby facilitating export from the endoplasmic reticulum and promoting expression on the cell surface. Thus, Cavββ regula! tes the ubiquitination and stability of the calcium channel complex. View full text Figures at a glance * Figure 1: Cavβ subunits increase surface and total expression of Cav1.2 channels. () Relative cell surface expression of HA-Cav1.2 channels measured by immunoluminometry in the presence of different subunits (*P < 0.01; ANOVA). Luminometry readings from nonpermeabilized cells (membrane) were normalized to those of permeabilized cells (total) to yield the fraction of channels at the cell surface. We conducted 3–5 ELISA assays for each condition. () Representative confocal microscope images of nonpermeabilized tsA-201 cells expressing the HA-Cav1.2 channel alone, or in combination with Cavβ1b, Cavα2-δ1, Cavβ1b+Cavα2-δ1. Scale bar, 10 μm. () Western blot of surface-biotinylated HA-Cav1.2 transfected into tsA-201 cells, coexpressed with Cavα2-δ1 or Cavβ1b. Lower panel, corresponding actin control. () Western blot of total HA-Cav1.2 transfected into tsA-201 cells, coexpressed with Cavα2-δ1 or Cavβ1b. () Quantification of relative integrated density values from western blots such as in panel (surface) and (total). Each HA-Cav1.2 band was correcte! d for the intensity of the actin control, and then normalized to the values obtained in the absence of ancillary subunits to facilitate comparison. Data from three experiments are included in the bar chart (*P < 0.05, **P < 0.01; ANOVA). Error bars show s.e.m. * Figure 2: Determinants of endoplasmic reticulum retention of intracelllular regions of HVA calcium channels. () Schematic representation of the different constructs of intracellular regions of Cav1.2 and Cav2.2, fused to CD4 membrane receptor. Cter, C terminus; Nter, N terminus. () Confocal images representing the translocation of Cavβ1b-YFP to the membrane mediated by CD4-I-II linker of Cav1.2. Note that the CD4-I-II linker of Cav3.2 does not translocate Cavβ1b-YFP. Scale bars, 10 μm. () Surface expression of intracellular linkers of Cav1.2 fused to the CD4 transmembrane region, measured by immunoluminometry. () Surface expression of intracellular linkers of Cav2.2 fused to CD4 transmembrane regions (*P < 0.05; one-way ANOVA followed by Tukey's multiple comparison tests). Error bars show s.e.m.; n = 3–5 ELISA assays for each condition. Fractional surface expression values for the fusion constructs were normalized to those of the cell surface expression marker CD4-AA in each transfection (see Online Methods). * Figure 3: MG132 and Cavβ both increase surface expression of Cav1.2 channels. () Confocal images of nonpermeabilized tsA-201 cells transfected and stained for HA-Cav1.2 after treatment with 5 μM of MG132 (right). Scale bars, 50 μm. () Western blot of total HA-Cav1.2 transfected into tsA-201 cells, coexpressed with Cavα2-δ1 or Cavβ1b, with or without treatment with the proteasome inhibitor MG132 (5 μM). Lower panel, corresponding actin control (input 5%). () Quantification of relative integrated density values from each band of the western blot in with or without MG132. Intensity of HA-Cav1.2 was normalized to that of the actin band. Data from five experiments are included (**P < 0.01; ANOVA). Error bars show s.e.m. * Figure 4: Cav1.2 channels interact with the ERAD protein complex in the absence of Cavβ. (–) Confocal images of permeabilized tsA-201 cells transfected and stained for the membrane marker CD4-AA (green) and HA-2 (red), coexpressed with Cavβ () or after overnight treatment with 5 μM of MG132 (). Inset images correspond to areas outlined by white dashed boxes. Scale bars represent 10 μm in main images and 5 μm in magnified images. Similar data were obtained from multiple transfections. In , there is no HA-2 label in the membrane region (arrows). () Summary of the intensity correlation quotients (ICQ) for the imaging conditions. The ICQ was measured for multiple transfections and denotes colocalization between HA-2 and CD4-AA in the presence but not the absence of Cavβ1b (**P < 0.01; ANOVA). () Left, co-immunoprecipitations of 2 and endogenous p97 (top) or derlin-1 (bottom) from transfected tsA-201 cells (left) or rat brain homogenate (right). In tsA-201 cells, 2 channels contained the HA tag, and the western blot was also probed for HA immunoreactivity. () ! Quantification of the relative integrated density value (RIU) from each band of co-immunoprecipitated p97 from tsA-201 cells. To account for variation in 2 expression, the RIU ratio between p97 and HA-2 was calculated, and the data were subsequently normalized to the condition of HA-2+MG132. Data from three experiments are included in the bar chart (*P < 0.05; ANOVA). () As in , but for derlin-1 rather than p97. Data from three experiments are included in the bar chart (*P < 0.05; ANOVA). Error bars show s.e.m. * Figure 5: The Cavβ subunit prevents ubiquitination of Cav1.2 channels. () Western blot of HA-Cav1.2 and ubiquitinated Cav1.2 with or without treatment with MG132. Channels were immunoprecipitated with HA antibody. Membranes blotted for HA were stripped and blotted with ubiquitin antibody. () Western blot showing ubiquitination of immunoprecipitated HA-Cav1.2 channels, in tsA-201 cells transfected with or without Cavβ1b and treated with MG132. Membranes were probed for ubiquitin (upper) and actin (lower). () As in but using the Cavβ binding–deficient Cav1.2W440A mutant. Also included is a representative co-immunoprecipitation experiment showing that, unlike the wild-type channel, the Cav1.2W440A channel does not associate with a flag-tagged Cavβ2a construct. () Western blot showing ubiquitination of immunoprecipitated Cav1.2 channels in CAD cells. Membranes were probed for ubiquitin (upper) and actin (lower). () Left, western blot of Cav1.2 from cultured rat hippocampal neurons. Neurons were incubated with MG132 overnight. Lysates were immu! noprecipitated with Cav1.2 antibody. Right, membranes blotted for Cav1.2 were stripped and blotted with ubiquitin antibody. * Figure 6: Cav1.2 channels associate with the RING domain ubiquitin ligase RFP2. () Western blot of ubiquitinated 2 (2nd lane) or 2 transfected with shRNA for RFP2 (3rd lane). Membranes were probed for actin (lower). () Western blot of ubiquitinated 2 from immunoprecipitates of 2 from CAD cells treated with MG132 (10 μM) and transfected with different shRNA plasmids (gp78 and Hrd-1). Membranes were also probed for 2 (lower). () Densitometric analysis of ubiquitinated 2 channels (,) in relation to total 2 protein, for different conditions of shRNA transfection. Data from three experiments are included (*P < 0.05; ANOVA). Error bars show s.e.m. () Co-immunoprecipitation experiments from CAD cells. Immunoprecipitates of 2, RFP2 and control (IgG) were run on SDS-PAGE and membranes were blotted with RFP2 antibody. Actin was run as loading control (bottom). RPF2 was detected at a molecular weight of 47 kDa and control IgG runs above 50 kDa (right). () Co-immunoprecipitation experiments from rat brain homogenates in the presence of MG132. Immunoprecipitates of! 2, control (IgG) and RFP2 were run on SDS-PAGE and blotted with 2 antibody. Consistent with ubiquitination, higher molecular weight 2 bands were present when channels were precipitated with RFP2 antibodies. () Co-immunoprecipitation experiments from tsA-201 cells transfected with HA-2 or HA-2 + WT-RFP2-YFP in the presence of MG132. Immunoprecipitates of HA-2 were run on SDS-PAGE and blotted with YFP antibody. () Western blot of ubiquitinated HA-2 from immunoprecipitates of HA-2 transfected into tsA-201 cells in the absence or the presence of WT-RFP2. Experiments were conducted in the presence of MG132. Actin was run as loading control (bottom). () Western blot of ubiquitinated HA-2 from immunoprecipitates of HA-2 transfected in tsA-201 cells in the absence or the presence of DN-RFP2. Experiments were conducted with MG132. Actin was run as loading control (bottom). * Figure 7: Dominant-negative RFP2 increases barium current density, and enhances surface expression of Cav1.2 channels in hippocampal neurons. () Whole-cell current-voltage relations from CAD cells under control conditions, or when transfected with DN-RFP2. Error bars show s.e.m. () Confocal image obtained from a cultured hippocampal neuron transfected with eGFP and HA-Cav1.2 and immunostained with HA antibody. Lower panel, merged image. (,) Higher magnifications image of a neuronal process expressing eGFP and labeled for HA-Cav1.2, with WT-RFP2 or DN-RFP2. Scale bars, 10 μm; similar data were obtained from multiple transfections. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to this work. * Christophe Altier & * Agustin Garcia-Caballero Affiliations * Department of Physiology and Pharmacology, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada. * Christophe Altier, * Agustin Garcia-Caballero, * Brett Simms, * Haitao You, * Lina Chen, * Jan Walcher, * H William Tedford, * Tamara Hermosilla & * Gerald W Zamponi Contributions C.A., A.G.-C. and G.W.Z. designed the study and wrote the manuscript. G.W.Z. supervised the study. C.A., A.G.-C., B.S., H.Y. and L.C. performed experiments and data analysis. J.W. and H.W.T. contributed to molecular biology. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Gerald W Zamponi Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (6M) Supplementary Figures 1–10 Additional data
• APCCdh1 mediates EphA4-dependent downregulation of AMPA receptors in homeostatic plasticity
  - Nat Neurosci 14(2):181-189 (2011)
  Nature Neuroscience | Article APCCdh1 mediates EphA4-dependent downregulation of AMPA receptors in homeostatic plasticity * Amy K Y Fu1 Search for this author in: * NPG journals * PubMed * Google Scholar * Kwok-Wang Hung1 Search for this author in: * NPG journals * PubMed * Google Scholar * Wing-Yu Fu1 Search for this author in: * NPG journals * PubMed * Google Scholar * Chong Shen1 Search for this author in: * NPG journals * PubMed * Google Scholar * Yu Chen1 Search for this author in: * NPG journals * PubMed * Google Scholar * Jun Xia1 Search for this author in: * NPG journals * PubMed * Google Scholar * Kwok-On Lai1 Search for this author in: * NPG journals * PubMed * Google Scholar * Nancy Y Ip1 Contact Nancy Y Ip Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:181–189Year published:(2011)DOI:doi:10.1038/nn.2715Received16 August 2010Accepted17 November 2010Published online26 December 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Homeostatic plasticity is crucial for maintaining neuronal output by counteracting unrestrained changes in synaptic strength. Chronic elevation of synaptic activity by bicuculline reduces the amplitude of miniature excitatory postsynaptic currents (mEPSCs), but the underlying mechanisms of this effect remain unclear. We found that activation of EphA4 resulted in a decrease in synaptic and surface GluR1 and attenuated mEPSC amplitude through a degradation pathway that requires the ubiquitin proteasome system (UPS). Elevated synaptic activity resulted in increased tyrosine phosphorylation of EphA4, which associated with the ubiquitin ligase anaphase-promoting complex (APC) and its activator Cdh1 in neurons in a ligand-dependent manner. APCCdh1 interacted with and targeted GluR1 for proteasomal degradation in vitro, whereas depletion of Cdh1 in neurons abolished the EphA4-dependent downregulation of GluR1. Knockdown of EphA4 or Cdh1 prevented the reduction in mEPSC amplitude in! neurons that was a result of chronic elevated activity. Our results define a mechanism by which EphA4 regulates homeostatic plasticity through an APCCdh1-dependent degradation pathway. View full text Figures at a glance * Figure 1: Activation of EphA4 reduces synaptic strength. (–) Cortical neurons (16 DIV) were treated with Fc, ephrin-A1 or ephrin-B1 (5 μg ml−1) for 4 h, and the mEPSC was recorded. () Representative mEPSC traces. () Compared to the Fc-treated control, the mean frequency of mEPSCs decreased significantly after treatment with ephrin-A1 but not ephrin-B1. Data are expressed as mean ± s.e.m.; *P < 0.05, ANOVA with Student-Newman-Keuls test (4 experiments; >10 neurons recorded from each experiment). () Cumulative amplitude distribution of mEPSCs in cortical neurons after treatment with Fc, ephrin-A1 or ephrin-B1 for 4 h. There is a leftward shift of cumulative amplitude distribution of mEPSC upon ephrin-A1 treatment. () Knockdown of EphA4 by shRNA abolished the reduction in mEPSC amplitude triggered by ephrin-A1. Cortical neurons (12–14 DIV) were transfected with GFP and pSUPER-EphA4 shRNA (shEphA4) or pSUPER vector (Control). Neurons at 19–21 DIV were treated with ephrin-A1 for 16 h. The mEPSC was recorded for neurons that e! xpressed GFP. Data are presented as mean ± s.e.m. from three experiments (n > 10 neurons from each experiment; *P < 0.05, ANOVA followed by Student-Newman Keuls test). * Figure 2: Ephrin-A1 downregulates the expression of GluR1 at synapses through activation of EphA4. () Confocal images show punctate staining of GluR1 and PSD-95 in hippocampal neurons. GluR1 clusters co-localized with PSD-95 (arrows) decreased after ephrin-A1 treatment; PSD-95 clusters that lacked significant GluR1 immunoreactivity (arrowheads) were frequently found in ephrin-A1-treated neurons. Scale bars, 10 μm. () Quantification of PSD-95 and GluR1 clusters. ***P < 0.005; ANOVA with Mann-Whitney Rank Sum Test. () Quantification of synaptic localization of GluR1 and GluR2, as indicated by the percentage of PSD-95 that co-localized with GluR clusters. ***P < 0.005; Student's t-test. () Time-lapse imaging of hippocampal neurons expressing GluR1-GFP after treatment with ephrin-A1 for 4–6 h. Representative images show that some GluR1 clusters (arrowheads) were more stable, whereas others (arrows) disappeared during the imaging period. () Quantification of the loss of GluR1-GFP clusters (n = 11 for Fc, n = 12 for ephrin-A1; *P < 0.05, Student's t-test). () Ephrin-A1-media! ted reduction in total GluR1 expression depends on EphA4. Cortical neurons prepared from EphA4−/− mice or EphA4+/+ littermates were treated with Fc or ephrin-A1 for 24 h. Different amounts of protein (5–15 μg) were loaded. () Quantitative analysis of GluR1 protein level (3 experiments; *P < 0.05, Student's t-test). () Ephrin-A1 reduced both surface and total GluR1. () Increased GluR1 in synaptosomes of EphA4−/− mouse brains. Crude synaptosomal fractions of whole brains from adult EphA4+/+ (+/+) or EphA4−/− (−/−) mice were prepared, and western blot analysis was performed. * Figure 3: Chronic elevation of synaptic activity reduces GluR1 expression in an EphA4-dependent manner. () Bicuculline-induced tyrosine phosphorylation of EphA4 (p-EphA4). () Quantification analysis of p-EphA4 (3 experiments; *P < 0.05, **P < 0.01, ***P < 0.005; ANOVA with Student-Newman-Keuls test). () Cortical neurons were pretreated with EphA4-Fc for 0.5 h, and then treated with bicuculline (Bic) for 1 h (3 experiments). () Fold change in p-EphA4 (**P < 0.05; ANOVA with Student-Newman-Keuls test). () EphA4 was required for the reduction in total GluR1 in response to chronic treatment with bicuculline. Cortical neurons from EphA4+/+ (n = 5) and EphA4−/− mice (n = 3; from 3 experiments) were treated with bicuculline for 24 h. () Quantification analysis for total GluR1 (P < 0.05; Student's t-test). (–) Bicuculline treatment reduced the number of GluR1 clusters. (,) Hippocampal neurons were treated with bicuculline for 24 h, after which neurons were subjected to immunocytochemical analysis using GluR1 and PSD-95 antibodies. () Representative confocal images showed punctat! e staining of GluR1 and PSD-95. () The number of PSD-95 and GluR1 clusters was reduced after bicuculline treatment (3 experiments; *P < 0.05, ***P < 0.001; ANOVA with Mann-Whitney Rank Sum test). () EphA4 is required for the reduction of mEPSC amplitude induced by chronic bicuculline treatment. The mEPSC amplitude in shEphA4-transfected cortical neurons after bicuculline treatment (3 experiments; *P < 0.05; ANOVA, with Student-Newman-Keuls test). () Co-treatment with bicuculline and ephrin-A1 does not further reduce the mEPSC amplitude. Cortical neurons were treated with ephrin-A1 (A1), bicuculline or bicuculline with ephrin-A1 (4 experiments; ***P < 0.005; ANOVA with Student-Newman-Keuls test). * Figure 4: Ephrin-A-EphA4 signaling reduces GluR1 expression by a proteasome-dependent pathway. (,) EphA4-dependent degradation of GluR1 is mediated by a proteasome-dependent pathway. () HEK293T cells were transfected with GluR1 and EphA4, and were then treated with MG132 (10 μM), lactacystin (Lac, 10 μM), chloroquine (CHQ, 50 μM) or ammonium chloride (NH4Cl, 20 mM) for 5 h. () Inhibition of proteasome-mediated degradation abolished the downregulation of GluR1 by ephrin-A1. Cortical neurons were treated with MG132 for 0.5 h before stimulation by ephrin-A1 for 7–16 h in the presence of the inhibitor. () Quantitative analysis of GluR1 (n = 4; *P < 0.05, ANOVA with Student-Newman Keuls test). () Ephrin-A1 reduced mEPSC amplitude through proteasome-mediated degradation. Cortical neurons were treated with MG132 for 0.5 h and then with ephrin-A1 for 16 h. The mEPSC amplitude was measured (≥3 experiments; *P < 0.05, ANOVA with Student-Newman Keuls test). () Ephrin-A1 induced polyubiquitination of GluR1 in neurons. Cultured cortical neurons were treated with MG132 for 0! .5 h before treated with ephrin-A1 for 6 h. Whole-cell lysate was immunoprecipitated with GluR1 antibodies, followed by immunoblotting with anti-ubiquitin (Fk2) antibody which recognizes both mono- and polyubiquitinated proteins (n = 4). Similar results were observed when anti-polyubiquitin (Fk1) antibodies were used for immunoblotting (data not shown). More polyubiquitinated protein was immunoprecipitated by GluR1 antibodies from neurons treated with ephrin-A1 than from control neurons treated with Fc. () Ubiquitinated GluR1 was detected in synaptosomes. GluR1 protein was immunoprecipitated from the synaptosomal fractions, followed by western blot analysis using the Fk2 antibody (n = 3). * Figure 5: E3 ubiquitin ligase complex APCCdh1 interacts with EphA4. () EphA4 interacted with APC2. Expression constructs encoding EphA4 and full-length APC2 were overexpressed in HEK293T cells separately. EphA4 was immunoprecipitated using EphA4 antibody and then pulled down by proteinG-Sepharose beads (IgG served as the control). The beads were then incubated with APC2-expressing cell lysate (400 μg or 800 μg as indicated). APC2 protein pull down by EphA4 was examined by western blot analysis. () Ephrin-A1 increased the interaction between APC2 and EphA4 in neurons. Cortical neurons (14 DIV) were treated with Fc or ephrin-A1 for indicated durations. Lysate was immunoprecipitated with APC2 antibody and immunoblotted with antibodies to EphA4. Similar amounts of protein were subjected to immunoprecipitation for different treatments, as indicated by immunoblotting the lysate (INP) with antibodies to EphA4 and APC2. () APC2 associated with EphA4 and GluR1 in rat brain in vivo. Rat brain homogenate (postnatal day (P)7, P30 and adult (Ad)) was i! mmunoprecipitated with antibodies to APC2 and immunoblotted with antibodies to EphA4, GluR1 or APC2. () Adult rat brain fractions separated by differential centrifugation and extraction were subjected to western blot analysis for APC2, Cdh1, GluR1 and EphA4. Synaptophysin (SYN) served as the negative control for the different PSD fractions. P1: total brain lysates; S3: cytosolic fraction; SPM: synaptic plasma membrane. SPM was further extracted by Triton X-100 once (PSD 1T), twice (2T), or with Triton X-100 followed by Sarkosyl (1T+S). * Figure 6: Polyubiquitination and degradation of GluR1 requires APCCdh1. () Polyubiquitination of GluR1 required Cdh1. HEK293T cells were transfected with plasmids as indicated (n = 3). () Cdh1 interacted with the N-terminal region of GluR1. HEK293T cells were transfected with Cdh1 or GluR1 fragment (N-GluR1, amino acids 1–404 or C-GluR1, amino acids 405–907). Cdh1 protein was pulled down by Cdh1 antibody, and was then incubated with cell lysate expressing GluR1 fragments (200 or 500 μg). Input, 10 μg. () Degradation motifs on GluR1 are important for GluR1-Cdh1 interaction. HEK293T cells were transfected with Cdh1 and GluR1 or its 3M mutant. () Cdh1 reduced surface and total GluR1 in HEK293T cells. () Blockade of proteasome-dependent degradation partially inhibits the Cdh1-dependent degradation of GluR1. HEK293T cells overexpressing GluR1 or GluR1 with Cdh1 were pulse-labeled with [35S]-methionine and chased for 2 h. () The integrity of D-box motifs on GluR1 was required for Cdh1-dependent degradation of GluR1 in HEK293T cells; GluR1 (WT) o! r its single point mutants (D-43M, D-126M or A-159M). () Expression of ubiquitin mutant (K48R) inhibited the Cdh1-dependent degradation of GluR1. () Simultaneous mutation of lysine residues 831, 837, 840 and 886 to arginine (4KR) could not stabilize the expression of GluR1 when co-transfected with Cdh1. () Knockdown of Cdh1 inhibited the ephrin-A1-dependent reduction of GluR1. Cortical neurons were transfected with Cdh1 siRNA and then treated with ephrin-A1. The reduced expression of Cdh1 after EphA4 activation may be due to auto-degradation as Cdh1 is a target of APCCdh148. * Figure 7: Reduction of synaptic strength by ephrin-A1 or chronic treatment of bicuculline depends on APCCdh1-dependent proteasome degradation pathway. (,) Expression of Cdh1 ΔWD40 mutant abolished the reduction in synaptic strength after ephrin-A1 treatment. Cortical neurons (14–16 DIV) were transfected with Cdh1 ΔWD40 and GFP, and then treated at 20–22 DIV with ephrin-A1 for 16–24 h. () Representative traces. () Quantification of mEPSC amplitude after ephrin-A1 treatment (from three independent experiments; *P < 0.05, ANOVA with Student-Newman Keuls test). (,) Expression of shRNA-resistant Cdh1 in Cdh1-depleted neurons restored the ephrin-A1-dependent reduction in mEPSC amplitude. Cortical neurons were transfected with shCdh1 together with the Cdh1-WT or shRNA-resistant Cdh1 expression constructs, then treated with ephrin-A1. () Representative traces. () Quantification of mEPSC amplitude (from three experiments; *P < 0.05, ANOVA with Student-Newman Keuls test). (–) Inhibition of Cdh1 or knockdown of Cdh1 in neurons abolished the reduction in mEPSC amplitude after treatment with bicuculline for 16–24 h. Cortica! l neurons were transfected with Cdh1 ΔWD40 and GFP (,) or pSUPER-Cdh1 shRNA (shCdh1) or pSUPER vector (Control) together with GFP (,), and then treated with bicuculline (Bic) for 16–24 h. (,) Representative traces. (,) Quantification of mEPSC amplitude upon bicuculline treatment. Data are expressed as mean ± s.e.m. (>10 neurons recorded from each experiment, ≥3 experiments; *P < 0.05, ANOVA with Dunn's test). Author information * Abstract * Author information * Supplementary information Affiliations * Department of Biochemistry, State Key Laboratory of Molecular Neuroscience and Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China. * Amy K Y Fu, * Kwok-Wang Hung, * Wing-Yu Fu, * Chong Shen, * Yu Chen, * Jun Xia, * Kwok-On Lai & * Nancy Y Ip Contributions N.Y.I. supervised the project. A.K.Y.F., K.-W.H., W.-Y.F., K.-O.L. and N.Y.I. designed the experiments. K.-W.H., W.-Y.F., Y.C. and K.-O.L. conducted the majority of experiments. A.K.Y.F., K.-W.H., W.-Y.F., Y.C., K.-O.L. and N.Y.I. did the data analyses. J.X. designed and did the data analyses on the electrophysiology experiment and C.S. performed electrophysiology experiment. A.K.Y.F., K.-O.L. and N.Y.I. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Nancy Y Ip Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1–8 Additional data
• Autoregulatory and paracrine control of synaptic and behavioral plasticity by octopaminergic signaling
  - Nat Neurosci 14(2):190-199 (2011)
  Nature Neuroscience | Article Autoregulatory and paracrine control of synaptic and behavioral plasticity by octopaminergic signaling * Alex C Koon1 Search for this author in: * NPG journals * PubMed * Google Scholar * James Ashley1 Search for this author in: * NPG journals * PubMed * Google Scholar * Romina Barria1 Search for this author in: * NPG journals * PubMed * Google Scholar * Shamik DasGupta1 Search for this author in: * NPG journals * PubMed * Google Scholar * Ruth Brain1 Search for this author in: * NPG journals * PubMed * Google Scholar * Scott Waddell1 Search for this author in: * NPG journals * PubMed * Google Scholar * Mark J Alkema1 Search for this author in: * NPG journals * PubMed * Google Scholar * Vivian Budnik1 Contact Vivian Budnik Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:190–199Year published:(2011)DOI:doi:10.1038/nn.2716Received19 July 2010Accepted10 November 2010Published online26 December 2010Corrected online16 January 2011 Abstract * Abstract * Change history * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Adrenergic signaling has important roles in synaptic plasticity and metaplasticity. However, the underlying mechanisms of these functions remain poorly understood. We investigated the role of octopamine, the invertebrate counterpart of adrenaline and noradrenaline, in synaptic and behavioral plasticity in Drosophila. We found that an increase in locomotor speed induced by food deprivation was accompanied by an activity- and octopamine-dependent extension of octopaminergic arbors and that the formation and maintenance of these arbors required electrical activity. Growth of octopaminergic arbors was controlled by a cAMP- and CREB-dependent positive-feedback mechanism that required Octβ2R octopamine autoreceptors. Notably, this autoregulation was necessary for the locomotor response. In addition, octopamine neurons regulated the expansion of excitatory glutamatergic neuromuscular arbors through Octβ2Rs on glutamatergic motor neurons. Our results provide a mechanism for global! regulation of excitatory synapses, presumably to maintain synaptic and behavioral plasticity in a dynamic range. View full text Figures at a glance * Figure 1: Food-deprivation increase in larval locomotion is correlated with synaptopod formation at type II arbors. () NMJs at muscles 12 and 13 of a third-instar larva expressing mCD8-GFP in type II motor neurons, showing type I and type II endings (arrows), labeled with anti-HRP and anti-GFP. () Live imaging of type II endings through the cuticle of intact larvae before and after 2-h starvation. Arrows, synaptopods. () Locomotor speed of wild-type (Canton-S) larvae before and after 2-h starvation (n = 31, 26). () Number of synaptopods (pods) in fed and 2-h starved intact Tdc2>mCD8-GFP larvae (n = 10, 10). () Locomotor speed in the indicated genotypes (n = 31, 23, 29, 15, 34, 18, 25). () Percentage increase in locomotor speed in response to starvation in the indicated genotypes (n = 26, 25, 15, 14, 38, 14, 25). () Percentage increase in locomotor speed in response to light stimulation in the indicated genotypes (n = 20, 20, 16). () Ratio of EJP and mEJP amplitude upon bath application of 10 μM octopamine (n = 10 animals). () EJP and mEJP amplitude in tbh mutants (n = 6, 5 and 5 animals,! respectively). () Representative EJP traces in the indicated conditions. Scale bar, 8.5 μm (), 7.5 μm (). Error bars represent s.e.m. ***P ≤ 0.0001, **P ≤ 0.001, *P < 0.05. * Figure 2: Stepwise development of synaptopods. (–) Time lapse imaging of synaptopods in Tdc2>mCD8-GFP larvae showing the extension of synaptopods (, arrows), the formation of varicosities at the tip of synaptopods (, arrows) and the formation of a secondary synaptopod (, arrowhead) from a newly formed varicosity (arrow). Images of the same NMJs were taken 45 s apart. () Developmental time-lapse imaging of the same NMJ through the cuticle of first, second and third-instar larvae. Red arrow, a synaptopod developed into an entire branch. Orange arrow, a synaptopod was eliminated at third instar. Blue arrow, a synaptopod developed into a varicosity. Yellow and purple arrows, a varicosity developed into a new branch. () Time-lapse imaging as in in animals expressing mCherry and Syt1-GFP in type II endings in second and third instar. Arrow, synaptopod that acquired Syt1-containing varicosity. (–) Sequence of protein addition to an extending type II branch in Tdc2>mCD8-GFP preparations triple labeled with antibodies to GFP,! TBH and FasII (), Syt1 (), TBH (), Brp () or Futsch (). Arrows, sites of protein localization at synaptopods or newly formed varicosities. () Live imaging of synaptopods through the cuticle of intact Tdc2>mCD8-GFP larvae before and after starvation, showing the formation of varicosities (arrows) on top of synaptopods after starvation. () Sequence of synaptic protein addition at developing type II endings. () Percentage of synaptopods containing the indicated proteins at the stages of synaptopod, varicosity at the tip of a synaptopod (ball on pod) and secondary synaptopod (pod on ball). n (number of pod structures) is 30 for FasII, 30 for Syt1, 20 for TBH, 20 for Brp and 10 for Futsch. Scale bars, 10 μm (–), 22 μm (), 8.5 μm (), 2.5 μm (–) and 12 μm (). * Figure 3: Electrical activity and octopamine regulate the extension of synaptopods. (,) Live imaging of synaptopods before and after stimulation with high K+ () or octopamine () in Tdc2>mCD8-GFP larvae. () Net increase in synaptopod number ~2 h after K+ or ChR2 stimulation (see Online Methods for details; n = 8, 10, 10, 11). () Number of natural synaptopods in Tdc2>mCD8-GFP (WT control) and indicated genotypes (n = 31, 11, 31). () Net increase in synaptopod number in Tdc2>mCD8-GFP control (no drug) and preparations exposed to the indicated drugs (n = 11, 13, 8, 18). () Net increase in the number of synaptopods in response to different concentrations of octopamine in Tdc2>GFP larvae (n = 11, 7, 9, 11). () Net increase in synaptopod number at subthreshold concentration of octopamine or K+ depolarization in the presence of 0.1 mM Ca2+ (sub-Ca2+) in Tdc2>mCD8-GFP larvae (n = 8, 10, 11, 11, 13, 18, 13). () Number of type II boutons at muscle 12 (A3) of third-instar larvae in the indicated genotypes (n = 16, 10, 15, 14, 12, 6); wild-type, Canton-S. () Percentage ! increase in locomotor speed in response to food deprivation in the indicated genotypes (n = 31, 20, 34, 18, 25); WT control, Canton-S. Scale bar, 7 μm (,). Error bars represent s.e.m. ***P ≤ 0.0001, **P ≤ 0.001, *P < 0.05. * Figure 4: Innervation and maintenance of type II arbors depends on activity. (,) NMJs at muscles 12 and 13 in preparations expressing mCD8-GFP in octopamine neurons and double labeled with anti-GFP and anti-HRP antibodies in Tdc2>mCD8-GFP () and Tdc2>mCD8-GFP, Kir2.1 () third-instar larvae. (,) Type II motor neuron axons labeled with anti-GFP and anti-HRP emerging from the CNS at the segmental nerves (, arrows) and terminating close to the NMJ (, arrow) in GFP, Kir2.1-type II preparations stained with antibodies to HRP and GFP. (–) NMJs at muscles 12 and 13 in preparations expressing mCD8-GFP in octopamine neurons and double labeled with anti-HRP and anti-TBH antibodies in wild-type (,) and Tdc2>Kir2.1 (,) larvae at the first (,) and second-instar (,) larval stages. Arrows point to type II boutons. (–) Third-instar type II NMJs from Kir2.1–type II, Gal80ts larvae shifted to 29 °C at the stages indicated in the time scale at the top of each panel, double stained with anti-HRP and anti-TBH. Arrows point to breaks in the arbors or debris. () Perc! entage of intact, broken or absent (denervated) type II NMJs in Tdc2>Kir2.1, TubP-Gal80ts (Kir2.1–type II, Gal80ts) larvae shifted to 29 °C at the indicated stages (n = 25, 24, 46, 22). Scale bar, 24 μm (,), 12 μm (,–), 38 μm () and 10 μm (–). * Figure 5: Synaptopod extension is regulated by the cAMP pathway and requires new protein synthesis and CREB. (–) Live imaging of synaptopods (arrows) in Tdc2>mCD8-GFP (, WT control), dncM14, Tdc2>mCD8-GFP flies () and Tdc2>mCD8-GFP, PACα flies () before and after light stimulation. () Number of natural synaptopods in the indicated genotypes (n = 175, 21, 11, 8, 17, 11, 24, 11). (–) Net increase in synaptopod number in preparations expressing PACα in wild type and tbh mutant background and subjected to the indicated light procedures (, n = 10, 12, 12, 11, 12), upon application of octopamine (OA) in the indicated genotypes (, n = 11, 13, 10, 11, 12, 8) and in Tdc2>mCD8-GFP preparations treated with actinomycin or cycloheximide, or expressing Tdc2>CREBdn and exposed to octopamine as indicated (, n = 11, 13, 18, 11, 13; see Online Methods for details of PAC and octopamine stimulation procedures). () Locomotor speed of the indicated genotypes (n = 31, 18, 15, 21, 15). () Percentage increase in locomotor speed in response to food deprivation in the indicated genotypes (n = 26, 18, ! 15, 21, 15). Scale bar, 14 μm (–). Error bars represent s.e.m. ***P ≤ 0.0001, **P ≤ 0.001, *P < 0.05. * Figure 6: Presynaptic Octβ2R autoreceptors, but not OAMB receptors, regulate the growth of type II arbors. () Number of natural synaptopods in the indicated genotypes (n = 175, 12, 13, 25, 14); WT control is Tdc2>mCD8-GFP. () Number of type II boutons in the indicated genotypes (n = 16, 12, 16, 16, 15, 11, 11, 10, 12, 12, 13, 12); WT control is Canton-S; type II driver control is Tdc2>Dcr; Octβ2R-RNAi–type II is Tdc2>Dcr, Octβ2R-RNAi. () Net increase in the number of synaptopods in the indicated conditions and genotypes (n = 11, 13, 11, 10, 12, 11, 14; WT control is Tdc2>mCD8-GFP). () Percentage increase in locomotor speed in the indicated genotypes in response to food deprivation (n = 26, 16, 13, 15, 13, 17, 15, 13, 13, 15). () Locomotor speed in the indicated genotypes (n = 31, 20, 17, 17, 15, 18, 17, 13, 13, 17). For and , WT control is Canton-S; type II driver control is Tdc2>Dcr; type I+II driver control is C380>Dcr; muscle driver control is C57>Dcr; Octβ2R-RNAi–type II is Tdc2>Dcr, Octβ2R-RNAi; Octβ2R-RNAi–type I+II is C380>Dcr, Octβ2R-RNAi; Octβ2R-RNAi-muscle ! is C57>Dcr, Octβ2R-RNAi. Error bars represent s.e.m. ***P ≤ 0.0001, *P < 0.05. * Figure 7: Type II motor neurons regulate the growth of type I arbors. () Number of type I boutons at muscle 6 and 7 (A3) in third-instar larvae after eliminating either type II motor neurons or octopamine production from type II motor neurons (n = 12, 17, 21, 18, 20, 13). () Same data from larvae with decreased Octβ2R levels (N = 12, 36, 17, 18, 12, 13, 11). WT controls are Canton-S. Error bars represent s.e.m. ***P ≤ 0.0001, **P ≤ 0.001, *P < 0.05. Change history * Abstract * Change history * Author information * Supplementary informationErratum 16 January 2011In the version of this article initially published online, an error was made on page 2, left column, third paragraph, sixth line. The genotype 'tbhnM19' should read 'tbhnM18'. In the Online Methods, left column, first paragraph, fifth line, the genotype 'tbhnM189' should read 'tbhnM18 (ref. 9)'. Finally, in the Online Methods, right column, seventh paragraph, fourth line, the abbreviation 'Drc' should read 'Dcr'. These errors have been corrected for the print, PDF and HTML versions of this article. Author information * Abstract * Change history * Author information * Supplementary information Affiliations * Department of Neurobiology, University of Massachusetts Medical School, Worcester, Massachusetts, USA. * Alex C Koon, * James Ashley, * Romina Barria, * Shamik DasGupta, * Ruth Brain, * Scott Waddell, * Mark J Alkema & * Vivian Budnik Contributions A.C.K. designed and performed most experiments and contributed to manuscript writing; J.A. contributed to tool development, electrophysiology, experimental design and manuscript writing; R.B. performed RT-PCR and some immunocytochemistry; S.W., S.D. and R.B. generated, characterized and validated PACα function; M.J.A. helped with the design of the TBH antibody; and V.B. directed the project and wrote the manuscript in collaboration with A.C.K. and J.A. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Vivian Budnik Supplementary information * Abstract * Change history * Author information * Supplementary information Movies * Supplementary Movie 1 (20K) Dynamics of natural synaptopods at type-II arbors. * Supplementary Movie 2 (256K) Synaptopods develop ball-shaped varicosities. * Supplementary Movie 3 (112K) Secondary synaptopod formation on a varicosity. * Supplementary Movie 4 (1M) Induction of synaptopod formation upon acute increase in cAMP levels. PDF files * Supplementary Text and Figures (896K) Supplementary Figures 1–8 Additional data
• Slow integration leads to persistent action potential firing in distal axons of coupled interneurons
  - Nat Neurosci 14(2):200-207 (2011)
  Nature Neuroscience | Article Computational and Systems Neuroscience Focus issue: February 2011 Volume 14, No 2 * * Reviews * Articles * Technical Report * * Contents * Editorial * Perspectives Slow integration leads to persistent action potential firing in distal axons of coupled interneurons * Mark E J Sheffield1 Search for this author in: * NPG journals * PubMed * Google Scholar * Tyler K Best1 Search for this author in: * NPG journals * PubMed * Google Scholar * Brett D Mensh2 Search for this author in: * NPG journals * PubMed * Google Scholar * William L Kath1, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Nelson Spruston1 Contact Nelson Spruston Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:200–207Year published:(2011)DOI:doi:10.1038/nn.2728Received27 August 2010Accepted01 December 2010Published online08 December 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The conventional view of neurons is that synaptic inputs are integrated on a timescale of milliseconds to seconds in the dendrites, with action potential initiation occurring in the axon initial segment. We found a much slower form of integration that leads to action potential initiation in the distal axon, well beyond the initial segment. In a subset of rodent hippocampal and neocortical interneurons, hundreds of spikes, evoked over minutes, resulted in persistent firing that lasted for a similar duration. Although axonal action potential firing was required to trigger persistent firing, somatic depolarization was not. In paired recordings, persistent firing was not restricted to the stimulated neuron; it could also be produced in the unstimulated cell. Thus, these interneurons can slowly integrate spiking, share the output across a coupled network of axons and respond with persistent firing even in the absence of input to the soma or dendrites. View full text Figures at a glance * Figure 1: Persistent firing in Htr5b interneurons. () A biocytin-filled Htr5b-EGFP–positive interneuron near the stratum radiatum–SLM border of hippocampal area CA1 (dendrites, blue; axon, red). A schematic representation of a CA1 pyramidal cell is also shown (DG, dentate gyrus). () Whole-cell current-clamp recording of persistent firing. To evoke persistent firing, we delivered a 1-s current step of 500 pA during each 10-s sweep (left, sweeps 1–4; right, sweeps 5–13). In this example, the total number of evoked action potentials before persistent firing was 1,151. () Persistent firing was also induced with 1-s current steps starting at 40 pA and incrementing the amplitude by 20 pA with each subsequent step (shown here is the response to the 11th step, 240 pA, which induced persistent firing after a total of 296 action potentials). In this example, persistent firing lasted over 1 min. The instantaneous firing frequency of each action potential is plotted below the recording. () Three representative cells showing the ! frequency of persistent firing over time after its onset. () Persistent firing duration measured from its onset to the last spike (n = 274). The red bar shows the cells where persistent firing outlasted the 4-min recording period (n = 5). () Persistent firing in a layer 2/3 neocortical interneuron (somatosensory cortex) induced with the same protocol used in ; only the final trace is shown. All data are from Htr5b-EGFP–positive hippocampal interneurons near the stratum radiatum–SLM border. * Figure 2: In vivo firing patterns induce persistent firing. () Persistent firing evoked by low () and high () frequency in vivo firing patterns. Bottom left, expanded segment of the high frequency in vivo firing pattern and corresponding evoked spikes. Asterisks indicate spikes that have no corresponding stimulus and thus indicate the onset of persistent firing during the stimulus period. () Comparison of the fraction of cells that generated persistent firing with various stimulation protocols. Step/pause (n = 19) is the protocol described in Figure 1c and Supplementary Figure 1a; low (n = 16) and high (n = 14) frequency refer to the in vivo firing patterns shown in and , respectively. () The total number of evoked spikes needed to generate persistent firing showed no difference between the low- and high-frequency protocols, but both were less than the step/pause protocol. () Within-cell comparisons (n = 10). () Grouped data comparisons (step/pause, n = 19; low frequency, n = 16; high frequency, n = 14). () The latency to persistent ! firing was shortest for the high-frequency protocol. () Within cell comparisons (n = 10). () Grouped data comparisons (step/pause, n = 19; low frequency, n = 16; high frequency, n = 14). Latency to persistent firing was greatest using the step/pause protocol. All summary data consist of mean ± s.e.m. ***P < 0.001, **P < 0.01, *P < 0.05. All data are from Htr5b-EGFP–positive hippocampal interneurons near the stratum radiatum–SLM border. * Figure 3: Full-sized action potentials and large and small spikelets during persistent firing match antidromic full and partial spikes. () Persistent firing with somatically evoked (for example, black arrow) and persistent firing (for example, green arrow) action potentials indicated. () Somatically evoked (black) and persistent firing (green) action potentials, peak aligned. () Antidromic (blue) and somatically evoked action potentials (black) from the same cell in , peak aligned. () Phase plot (dV dt−1 versus V) of action potentials from and (stimulus artifact eliminated). The numbers 1 and 2 on all phase plots indicate presumed axonal and somatic firing, respectively. () Spontaneous large spikelets during persistent firing (n = 6). () Expanded view (top) of large spikelets (blue) and a full-sized spike (green) taken from (blue and green arrows). Phase plots (bottom) of a large spikelet (blue), evoked (black) and persistent-firing action potentials (green) are shown. () Hyperpolarization in this cell revealed small spikelets. () Top left, expanded view of the spikelets in . Phase plots (bottom) of a smal! l spikelet (red), evoked (black) and persistent-firing (green) action potentials are shown. Top right, expanded view of the initial part of the phase plot. () Full action potential (green) and large (black and blue) and small spikelets (red) evoked by antidromic stimulation during somatic hyperpolarization to −125 mV in the presence of glutamate and GABA receptor blockers. () Colored spikes from overlaid and aligned by the stimulus artifacts. () Phase plot from the spikes in (right) with expanded view (left). All data are from Htr5b-EGFP–positive hippocampal interneurons near the stratum radiatum–SLM border. * Figure 4: Simulation of small and large spikelets indicates failure of antidromic action potentials at different locations along the axon. () Morphology of a stylized interneuron model, with a spherical soma connected to a primary axon with five side branches. () Somatic voltage trace in the stylized model as a result of distal axonal depolarization (960 μm from soma) during simultaneous somatic hyperpolarization. The resulting antidromic action potentials occurred in a repeating pattern consisting of a full action potential (green), an antidromic action potential that fails at the soma (blue) and three small spikelets (red, only one shown) corresponding to action potential failure at an axonal branch point. () Phase plot of traces from . Inset shows expanded view. () Morphology of a fully reconstructed interneuron with its cell body near the stratum radiatum–SLM border showing soma and dendrites (blue), axon (red) and locations of the axonal and somatic stimulating and recording electrodes. Inset, expanded view showing the two different points at which antidromic axonal action potentials fail. () Voltage tr! ace in the full morphological model as a result of distal axonal depolarization (325 μm from the soma) during simultaneous somatic hyperpolarization. Antidromic action potentials were generated and produced a repeating somatic voltage pattern consisting of a full action potential (green), an antidromic action potential that fails at the soma (blue) and an antidromic action potential that fails at an axonal branch point (brown). () Phase plot of traces from . Inset shows expanded view. * Figure 5: Persistent firing induced by antidromic stimulation and intercellular signaling. () Diagram depicting recording setup (axon red, dendrites blue). For antidromic stimulation, the stimulating electrode was placed in the molecular layer of the dentate gyrus to activate only axonal projections. () Antidromic stimulation of the axon during simultaneous somatic hyperpolarization also evoked persistent firing (in the presence of glutamate and GABA receptor blockers; see Online Methods). The inset shows failed spikes during antidromic stimulation. The number of stimuli was increased by two during each successive stimulus until persistent firing occurred. Persistent firing was reliably induced in this manner (n = 5). () Illustration of paired recording setup. Step current injections were delivered to cell 1 only. Interneurons near the stratum radiatum–SLM border and in the stratum radiatum were targeted. () Persistent firing was induced in the unstimulated cell and occurred before persistent firing was induced in the stimulated cell. No electrical coupling was ! observed between the pairs. In total, 19 Htr5b-EGFP–positive pairs were studied, with three showing this type of intercellular induction of persistent firing. * Figure 6: Calcium effects on persistent firing. () Recordings from an Htr5b-EGFP–positive hippocampal interneuron in normal (top; 2 mM) and low (bottom; 0.5 mM) Ca2+ artificial cerebrospinal fluid (ACSF). Note the firing lasts markedly longer in low Ca2+ than in normal ACSF. () Bar graph showing the number of evoked spikes required to induce persistent firing remains unchanged in low Ca2+ conditions (all data normalized to the 2 mM condition in the same cells; 1 mM, n = 9, 0.5 mM, n = 11; 0 mM, n = 10). Increasing the Ca2+ concentration to 5 mM slightly reduced the number of evoked spikes required to induce persistent firing (n = 4). () The duration of persistent firing was significantly increased in low Ca2+ (0.5 mM and 0 mM) and reduced in high Ca2+ (5 mM). All statistics are paired-sample comparisons relative to 2 mM Ca2+ in the same cell, *P < 0.05. All summary data are mean ± s.e.m. All data are from Htr5b-EGFP–positive hippocampal interneurons near the stratum radiatum–SLM border. * Figure 7: Gap junction blockers inhibit persistent firing. () Mefloquine (25 μM, ) and carbenoxolone (500 μM, ) were bath applied after three trials of persistent firing (sequential trial iteration indicated with number to the left of the trace) induction. Persistent firing was induced approximately once every 7 min (depending on how many spikes were required in each trial). The numbers above the evoked spikes indicate the total number of evoked action potentials for that trial. () The total number of evoked action potentials required to evoke persistent firing is plotted against the trial number (for 6 cells). Mefloquine (25 μM) was added to the bath after the third trial in each cell. In five out of the six cells persistent firing was not induced in the presence of mefloquine (represented by the red triangles indicating the maximum number of evoked spikes that was reached on that trial). () Carbenoxolone (500 μM) had a similar effect, preventing persistent firing in four out of four cells. All data are from Htr5b-EGFP–positi! ve hippocampal interneurons near the stratum radiatum–SLM border. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Neurobiology & Physiology, Northwestern University, Evanston, Illinois, USA. * Mark E J Sheffield, * Tyler K Best, * William L Kath & * Nelson Spruston * Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, Massachusetts, USA. * Brett D Mensh * Department of Engineering Sciences and Applied Math, Northwestern University, Evanston, Illinois, USA. * William L Kath Contributions All authors participated in the design of the experiments and the analysis and interpretation of the data. M.E.J.S. and T.K.B. performed the experiments. W.L.K. performed the simulations. N.S. and B.D.M. wrote the manuscript with input from the other authors. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Nelson Spruston Supplementary information * Abstract * Author information * Supplementary information Movies * Supplementary Movie 1 (6M) Simple model of axonal action potential propagation in a stylized axon. * Supplementary Movie 2 (11M) Model of axonal action potential propagation in a full morphological model. PDF files * Supplementary Text and Figures (5M) Supplementary Figures 1–7 and Supplementary Table 1 Additional data
• Biophysical mechanisms underlying olfactory receptor neuron dynamics
  - Nat Neurosci 14(2):208-216 (2011)
  Nature Neuroscience | Article Computational and Systems Neuroscience Focus issue: February 2011 Volume 14, No 2 * * Reviews * Articles * Technical Report * * Contents * Editorial * Perspectives Biophysical mechanisms underlying olfactory receptor neuron dynamics * Katherine I Nagel1 Search for this author in: * NPG journals * PubMed * Google Scholar * Rachel I Wilson1, 2 Contact Rachel I Wilson Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:208–216Year published:(2011)DOI:doi:10.1038/nn.2725Received30 August 2010Accepted25 November 2010Published online09 January 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The responses of olfactory receptor neurons (ORNs) to odors have complex dynamics. Using genetics and pharmacology, we found that these dynamics in Drosophila ORNs could be separated into sequential steps, corresponding to transduction and spike generation. Each of these steps contributed distinct dynamics. Transduction dynamics could be largely explained by a simple kinetic model of ligand-receptor interactions, together with an adaptive feedback mechanism that slows transduction onset. Spiking dynamics were well described by a differentiating linear filter that was stereotyped across odors and cells. Genetic knock-down of sodium channels reshaped this filter, implying that it arises from the regulated balance of intrinsic conductances in ORNs. Complex responses can be understood as a consequence of how the stereotyped spike filter interacts with odor- and receptor-specific transduction dynamics. However, in the presence of rapidly fluctuating natural stimuli, spiking simpl! y increases the speed and sensitivity of encoding. View full text Figures at a glance * Figure 1: Temporal patterns of ORN spiking are cell and odor dependent. Rasters and peristimulus time histograms (PSTHs; mean ± s.d.) show ORN spiking responses to an odor pulse (1.7 s duration, odors diluted in paraffin oil as labeled). The time course of the odor pulse (top) is not square because it is slightly smoothed by our odor delivery device (Supplementary Fig. 1). () An example of a response with a strong onset transient and offset inhibition. () In the same type of ORN, a different stimulus drove a similar steady-state firing rate, but the onset transient was weaker and there was no offset inhibition. () The same stimulus drove a strong onset transient and offset inhibition in a different type of ORN. () A more complex response, with an onset transient, then offset inhibition, then more excitation. () An example of inhibition followed by excitation. Each trace represents the mean of 5–6 sensillum recordings, each in a different fly. * Figure 2: Field potentials and spikes can be isolated from single ORNs. () Three types of sensillum in the maxillary palp4×18. () A typical extracellular recording from a pb1 sensillum (stimulus is 2-butanone 0.1×). Enlarged segment at the peak of the response shows individual spikes (inset); taking the first derivative (inset lower trace) facilitates spike detection. () TTX (50 μM) injected into the palp abolished spikes, leaving the LFP largely unaffected. We fit exponentials to the rising and falling phases of the LFP, and we also computed the overshoot after odor offset; none of these parameters changed significantly (data not shown). () In Or42a−/− flies, this type of sensillum no longer responded to this stimulus, although some spontaneous spikes persisted. () Mean pb1 LFP responses to selected stimuli were largely unaffected by TTX but were abolished by mutations in Or42a. This mutation did not affect responses to these stimuli in a different sensillum type (pb3; Supplementary Fig. 2). () In a pb2 sensillum, responses to fenchone (! 0.25×) and cyclohexanone (0.5×) were abolished by ablating the A neuron in a genetic background in which B was already ablated. In a background in which A was ablated, 1-octen-3-ol (1×) elicited an inhibitory (upward) LFP that was abolished by killing the B neuron. (The small remaining downward response reflects activity in other sensilla.) () In pb3 sensilla, LFP responses to isoamyl acetate (0.5×) were abolished by a mutation in Or85d. Here the A neuron was ablated genetically. Data in – represent mean ± s.d., n = 4–7 recordings each. * Figure 3: Filter models describe transformations between stimulus, LFP and spikes. () Time course of odor stimulus and neural response (pb1A; stimulus is 2-butanone 0.01×). Note that the spike rate was highest when the LFP was growing but suppressed when the LFP was recovering. () Linear filter describing the relationship between the stimulus and the LFP (pb1A, 2-butanone 0.01×; ± s.d.). Arrow indicates single filter lobe. () The prediction of the filter (blue) was simply an inverted and slightly smoothed version of the odor time course, as expected for a filter with a single lobe. Comparison to the recorded LFP (black, mean of five recordings in five flies) shows that the linear model is an adequate coarse description but underestimates onset rate and overestimates offset rate. () Actual versus predicted LFP for the stimulus segment shown in . Note that the plot bifurcates because the model underestimated responses during onset (open arrowhead) and overestimated responses during offset (filled arrowhead). () Linear filter describing the relationship be! tween LFP and spike rate. Arrows indicate two filter lobes. The biphasic filter means that the spike rate increased when the LFP was growing more negative, and was inhibited when the LFP was recovering. Because the negative lobe is larger than the positive lobe, the spike rate remained elevated above baseline during a maintained negative LFP deflection. () The prediction of the filter (magenta) agrees well with the data (black). () Actual versus predicted spike rate. * Figure 4: Odor and cell dependence of transduction and spiking dynamics. () Linear filters describing the relationship between the stimulus and the LFP for five different stimulus-cell combinations. The filter depends on both ligand and receptor. Mean ± s.d. across recordings, n = 5–6 each. Units of y axes are mV per unit odor. () Mean LFP responses for these ligand-receptor combinations (black). Colored lines show the prediction of the linear model, obtained by convolving the corresponding filter in with the stimulus waveform (top). Mean correlation coefficient, 0.93 ± 0.04. () Linear filters describing the relationship between the LFP and spike rate. These filters have a relatively stereotyped shape, unlike the transduction filters. Units of y axes are spikes per s per mV. () Mean spiking responses (black) in units of spikes per s. Colored lines show the prediction of the linear model, obtained by convolving the corresponding filter in with the recorded LFP. Mean correlation coefficient, 0.92 ± 0.06. * Figure 5: Knocking down DmNav makes the LFP-to-spiking transformation more differentiating. () Firing rates in ORNs with reduced expression of voltage-dependent Na+ channels (pb1A, stimulus is 2-butanone 0.1×). Thin lines are trial-averaged responses from different recordings (n = 20–22 sensilla in 10–11 flies of each genotype); thick lines indicate mean. Mean firing rate during the odor was significantly reduced (P < 0.01, t-test). () Spontaneous spike rate was also significantly reduced by Na+ channel knockdown (P < 0.01, t-test). () Decay from the peak odor-evoked firing rate was accelerated by Na+ channel knockdown, measured here by fitting an exponential to the trace from peak to 200 ms after odor offset (P < 0.01, t-test). () The ratio of peak to steady-state firing rate was significantly increased by Na+ channel knockdown (P < 0.01, t-test). () Knockdown had no effect on the time course of the LFP response, although the amplitude was slightly reduced. () Filters describing the LFP-to-spiking transformation (mean ± s.d., n = 4). Knockdown produced more ! symmetrical positive and negative lobes, indicating a more differentiating transformation. () Filters describing the stimulus-to-LFP transformation were unaffected by Na+ channel knockdown, as expected. * Figure 6: Dynamics of transduction and adaptation. () LFP recordings from pb1A ORNs illustrate how transduction dynamics depend on odor concentration. Top is 2-butanone (dilutions of 0.1, 0.02, 0.004, 0.0008, 0.00016 and 0.000032×). Bottom is isoamyl acetate (1, 0.2, 0.04, 0.008 and 0.0016×). Traces are means of 5–6 recordings. Traces at right are normalized to the same maximum negative deflection. Dashed blue line shows one exponential fit to response offset; note that only the initial segment was fit and no attempt was made to fit the overshooting later portion. () On and off rates as a function of odor concentration, mean ± s.d. across recordings. Rates were calculated by fitting exponential curves to the onset and offset phases of the normalized mean LFP. () A typical recording showing that a long adapting pulse of 2-butanone (0.1×) reduced the amplitude and onset slope of the LFP response to a weak test odor pulse (2-butanone 0.004×, green) but not the response to a strong test odor pulse (2-butanone 0.2×, orang! e). () Mean responses to test pulse 1 (solid) and test pulse 2 (dashed) for the two test odors shown in ; n = 6 recordings. Inset shows the onset phase of these traces normalized to the same amplitude. () Mean response amplitude (± s.d.) as a function of concentration for test pulse 1 (filled circles) and test pulse 2 (open circles). Arrows indicate the two concentrations shown in and . () Onset slope as a function of concentration for initial (filled circles) and adapted (open circles) responses. * Figure 7: Cross-adaptation between co-expressed odorant receptors. () A typical recording showing that a long pulse of 2-butanone (0.1×) acting on OR42a adapted the LFP response to a test pulse of another odor (pentyl acetate, 0.02×) acting on OR47a. Group data (right) show that both the amplitude and the onset rate of the second test pulse response were significantly reduced compared to the first test pulse response (P < 0.01, n = 6, paired t-test). () Same experiment, but in reverse: a long pulse of pentyl acetate (0.02×) adapted the response to a test pulse of 2-butanone (0.004×). The amplitude of the test pulse response was significantly reduced (P < 0.01, n = 5, paired t-test). The onset rate was reduced but not significantly (P = 0.14, paired t-test). () A long pulse of 1-octanol (0.1×) acting on OR47b de-adapted the response to a test pulse of 2-butanone (0.1×) acting on OR42a. Both the amplitude and the onset rate of the test pulse response were significantly increased (P < 0.01, n = 9, paired t-test). () A long pulse of 2-but! anone (0.1×) adapted the response to a test pulse of 1-octanol (0.1×). The amplitude of the test pulse response was significantly reduced (P < 0.01, n = 8, paired t-test). * Figure 8: Encoding the dynamics of natural odor plumes. () Plume dynamics depend on wind speed and odor location. Cartoon schematizes upwind distance (y) and crosswind distance (x). Traces are LFP recordings from a pb1A ORN responding to 2-butanone (0.1×). () Simultaneous recordings from a PID and a pb1A ORN (1.5 mm from the PID). Note spontaneous spikes (arrow); odor-evoked spikes are not visible at this scale. () Responses from the same sensillum in the same configuration with different odors. () Power spectra of simultaneously measured PID signals (solid lines) and LFP responses (dashed lines). Spectra are normalized to have the same total power. ORN is pb1A, stimuli are color coded as above (y = 5 cm, x = 0 cm). () LFP events sorted and averaged by amplitude (inverted here for display). The range of rise times (time from 10% to 90% of peak) was 28–32 ms. Stimulus was 2-butanone 0.1×. Same configuration as in and . () Average spike rates corresponding to the LFP events in . Dashed trace represents the largest average LFP r! esponse, normalized to the same amplitude as the highest average spike rate. () Peak LFP amplitude versus peak spike rate for the data in and , ± s.e.m. Open symbol, baseline (defined as the 30 ms starting 100 ms before event onset); dashed line, linear extrapolation from this to the largest response. () Power spectra of LFP (dashed line) and spike rate (dotted line) for responses shown in to 2-butanone 0.1×. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Neurobiology, Harvard Medical School, Boston, Massachusetts, USA. * Katherine I Nagel & * Rachel I Wilson * Howard Hughes Medical Institute, Harvard Medical School, Boston, Massachusetts, USA. * Rachel I Wilson Contributions K.I.N. performed the experiments and analyzed the data. K.I.N. and R.I.W. designed the experiments and wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Rachel I Wilson Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (6M) Supplementary Figures 1–7 Additional data
• Ephaptic coupling of cortical neurons
  - Nat Neurosci 14(2):217-223 (2011)
  Nature Neuroscience | Article Computational and Systems Neuroscience Focus issue: February 2011 Volume 14, No 2 * * Reviews * Articles * Technical Report * * Contents * Editorial * Perspectives Ephaptic coupling of cortical neurons * Costas A Anastassiou1, 2, 5 Contact Costas A Anastassiou Search for this author in: * NPG journals * PubMed * Google Scholar * Rodrigo Perin3, 5 Search for this author in: * NPG journals * PubMed * Google Scholar * Henry Markram3 Search for this author in: * NPG journals * PubMed * Google Scholar * Christof Koch1, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:217–223Year published:(2011)DOI:doi:10.1038/nn.2727Received06 October 2010Accepted30 November 2010Published online16 January 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The electrochemical processes that underlie neural function manifest themselves in ceaseless spatiotemporal field fluctuations. However, extracellular fields feed back onto the electric potential across the neuronal membrane via ephaptic coupling, independent of synapses. The extent to which such ephaptic coupling alters the functioning of neurons under physiological conditions remains unclear. To address this question, we stimulated and recorded from rat cortical pyramidal neurons in slices with a 12-electrode setup. We found that extracellular fields induced ephaptically mediated changes in the somatic membrane potential that were less than 0.5 mV under subthreshold conditions. Despite their small size, these fields could strongly entrain action potentials, particularly for slow (<8 Hz) fluctuations of the extracellular field. Finally, we simultaneously measured from up to four patched neurons located proximally to each other. Our findings indicate that endogenous brain ac! tivity can causally affect neural function through field effects under physiological conditions. View full text Figures at a glance * Figure 1: Simultaneous recordings from up to 12 electrodes inside and outside a single neuron in rat slice during intra- and extracellular stimulation. () Unipolar stimulation (I0 = 200 nA at 1 Hz) in slice via an extracellular pipette (S1) near the soma of a patched pyramidal neuron (intracellular pipette, I1). () Seven extracellular pipettes were positioned close to the soma of the patched neuron to monitor Ve (magenta, Ve recordings; black, mean waveform after 9-s stimulation). The isopotentials are shown in (blue, sink; red, source). () Ve amplitude as a function of pipette location for I0 = 50 (cyan), 100 (blue) and 200 (black) nA (circles, mean; error bars, s.d.). Distance is calculated from the tip of the extracellular stimulating electrode S1. Solid lines indicate the point-source approximation (least-squares fitting; typically the extracellular resistivity ρ = 2.5–3.8 Ωm, ref. 41). () Perturbing Ve (magenta) through extracellular stimulation from pipette S1 caused Vi to change (blue) through ephaptic coupling (top traces, I0 = 100 nA and f = 1 Hz; bottom traces, I0 = 100 nA and f = 8 Hz). () The membrane poten! tial Vm was defined as Vm = Vi − Ve. * Figure 2: Subthreshold extracellular field entrainment. () Ve (first row in magenta, mean in black), Vi (second row in blue) and Vm (third row in green) for one neuron for three stimulation regimes: slow and fast extracellular stimulation without intracellular depolarization (left and middle, respectively), and slow extracellular stimulation combined with sustained intracellular current injection (right). () Amplitude and phase (circles, mean; error bars, s.e.m.) of the Ve (magenta), Vi (blue) and Vm deflection (green) for extracellular stimulation frequencies of 1–100 Hz and constant I0 = 100 nA (n = 23 cells). Vi attenuation of an intracellular chirp without any extracellular field (blue line; chirp amplitude, 75 pA; frequency f = 3t where t (s) is time). () Amplitude and phase of the Ve (magenta), Vi (blue) and Vm deflection (green) as a function of membrane polarization (n = 17 cells; circles, mean; error bars, s.e.m.; stimulation frequency f = 8 Hz). () Normalized cross correlation (xcorr) between Vi and Ve (blue) as well ! as Vm and Ve (green) of the data shown in (line, mean; shadowed area, s.e.m.) for (left to right) f = 1, 8, 30 and 100 Hz. The difference between xcorr(Vi, Ve) and xcorr(Vm, Ve) for each frequency at one, two and three quarters of the inverse of the stimulation frequency was always highly significant (P < 0.001; paired t test Bonferroni-corrected for multiple comparisons). () xcorr(Vi, Ve; blue) and xcorr(Vm, Ve; green) for the data in (line, mean; shadowed area, s.e.m.) for (left to right) Iinj = −150, 0, 50 and 100 pA at f = 8 Hz. * Figure 3: Weak electric fields entrain spiking activity of individual neurons. () Ve (magenta) and Vm (green) without (control) and with an extracellular field (f = 1 Hz). () Normalized cross-correlation (xcorr) between Vi and Ve (blue) and Vm and Ve (green) of the low-pass (<100 Hz) suprathreshold data of an individual neuron without (top) and with extracellular stimulation (bottom) at f = 1 Hz and I0 = 200 nA. () STA spectra (same data as in ; top, control; bottom, extracellular stimulation). () Population vector analyses for f = 1 Hz (left to right, I0 = 25, 50, 100 and 200 nA; n = 25 neurons). Field entrainment of spikes led to nonuniform spike-phase distribution (P values by Rayleigh test) that was not attributable to changes in spike number (N(upper), control; N(lower), extracellular stimulation; Supplementary Fig. 3). () STA spectra (circles, mean; shadowed areas, s.e.m.) for the data in . For the control experiments, a (virtual) Ve identical to the subsequent extracellular stimulation experiment was assumed. () SFC (circles, mean; error bars, s! .e.m.) for extracellular stimulation (black) and control (cyan) experiments at (left to right) 1, 8 and 30 Hz as a function of stimulation strength (x axis: first row, circles indicate mean Ve amplitude at the soma and error bars indicate s.e.m.; second row, circles indicate mean E amplitude at the soma). Asterisks indicate statistical significance of the SFC difference between control and extracellular stimulation (paired t test, fdr-corrected for multiple comparisons; *P < 0.05, **P < 0.01, ***P < 0.001). The percentage increase in SFC relative to control is shown for statistically significant changes. STP, STA and SFC are shown for four individual neurons for all stimulation amplitudes and frequencies in Supplementary Figures 5, 7 and 9. * Figure 4: Ephaptic coupling leads to coordinated spiking activity among nearby neurons. () Four neurons with somata located within 100 μm of tissue were patched with intracellular electrodes (blue). Seven extracellular electrodes monitored Ve fluctuations (magenta). The extracellular stimulation electrode (S1) was 50–80 μm from the four somata. () Intracellular (black) and extracellular (magenta) activity during concurrent intracellular current injection to the four neurons (top, control; bottom, extracellular stimulation with I0 = 100 nA and f = 1 Hz). () Population vector analysis of all spikes from neurons 1–4 for (left to right) I0 = 25, 50, 100 and 200 nA and f = 1 Hz. As field strength increased, spikes from all neurons clustered around 270° (left to right, mean population vector angle, 197°, 272°, 260°, 262°) and the phase distribution significantly deviated from uniformity (P values by the Rayleigh test). There was no significant change in firing rate (P > 0.1) (N(upper), control; N(lower), extracellular stimulation). () STA spectra (circles,! mean; shadowed areas, s.e.m.) for the data in (cyan, control; black, extracellular stimulation). () SFC (circles, mean; error bars, s.e.m.) for extracellular stimulation (black) and control (cyan) experiments at (left to right) 1, 8 and 30 Hz as a function of stimulation strength (x axis: first row, circles indicate mean Ve amplitude at the soma, error bars indicate s.e.m.; second row, circles indicate mean E amplitude at the soma). Asterisks indicate a significant difference in SFC between control and extracellular stimulation experiments (paired t test, fdr-corrected for multiple comparisons; *P < 0.05). The percentage increase in SFC relative to control is shown for statistically significant changes. STP, STA and SFC for each neuron individually are shown in Supplementary Figures 11–13. Author information * Abstract * Author information * Supplementary information Primary authors * These authors contributed equally to the work. * Costas A Anastassiou & * Rodrigo Perin Affiliations * Division of Biology, California Institute of Technology, Pasadena, California, USA. * Costas A Anastassiou & * Christof Koch * Department of Bioengineering, Imperial College, London, UK. * Costas A Anastassiou * Laboratory of Neural Microcircuitry, EPFL, Lausanne, Switzerland. * Rodrigo Perin & * Henry Markram * Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea. * Christof Koch Contributions C.A.A. and C.K. designed the experiments. C.A.A. and R.P. performed the experiments. C.A.A. wrote the codes and analyzed the data. C.A.A., R.P., H.M. and C.K. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Costas A Anastassiou Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (14M) Supplementary Figures 1–17 and Supplementary Discussion Additional data
• Thalamic interneurons and relay cells use complementary synaptic mechanisms for visual processing
  - Nat Neurosci 14(2):224-231 (2011)
  Nature Neuroscience | Article Computational and Systems Neuroscience Focus issue: February 2011 Volume 14, No 2 * * Reviews * Articles * Technical Report * * Contents * Editorial * Perspectives Thalamic interneurons and relay cells use complementary synaptic mechanisms for visual processing * Xin Wang1, 3 Search for this author in: * NPG journals * PubMed * Google Scholar * Vishal Vaingankar1 Search for this author in: * NPG journals * PubMed * Google Scholar * Cristina Soto Sanchez1 Search for this author in: * NPG journals * PubMed * Google Scholar * Friedrich T Sommer2 Search for this author in: * NPG journals * PubMed * Google Scholar * Judith A Hirsch1 Contact Judith A Hirsch Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:224–231Year published:(2011)DOI:doi:10.1038/nn.2707Received16 September 2010Accepted21 October 2010Published online19 December 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Synapses made by local interneurons dominate the thalamic circuits that process signals traveling from the eye downstream. The anatomical and physiological differences between interneurons and the (relay) cells that project to cortex are vast. To explore how these differences might influence visual processing, we made intracellular recordings from both classes of cells in vivo in cats. Macroscopically, all receptive fields were similar, consisting of two concentrically arranged subregions in which dark and bright stimuli elicited responses of the reverse sign. Microscopically, however, the responses of the two types of cells had opposite profiles. Excitatory stimuli drove trains of single excitatory postsynaptic potentials in relay cells, but graded depolarizations in interneurons. Conversely, suppressive stimuli evoked smooth hyperpolarizations in relay cells and unitary inhibitory postsynaptic potentials in interneurons. Computational analyses suggested that these compleme! ntary patterns of response help to preserve information encoded in the fine timing of retinal spikes and to increase the amount of information transmitted to cortex. View full text Figures at a glance * Figure 1: Push-pull responses of an OFF-center relay cell and ON-center interneuron. () Anatomical reconstruction of an OFF-center relay cell. () Reconstruction of the dendrites of an ON-center interneuron (the axon was too pale to trace continuously). (,) Averaged responses of the membrane voltage to dark and bright disks flashed in the center of the receptive field in the relay cell () and the interneuron (). The icons on the left depict stimulus shape and contrast. (,) Averaged responses of the membrane voltage to annuli flashed in the surround of the receptive field in the relay cell () and interneuron (). The gray line under the traces marks the stimulus duration in –. * Figure 2: Receptive fields of relay cells and interneurons and prediction of neural responses using linear-nonlinear models. (,) Spatial () and temporal () receptive fields of two relay cells and two interneurons computed from the intracellular response (see ref. 5). Stimulus size is indicated by the yellow box. () Scatter plots of the actual intracellular response against that obtained using a linear filter made from the spatiotemporal receptive field show how the nonlinear component (red curve) of the model was fit. () Comparison of the actual and predicted responses. The actual response was normalized so that the mean was zero and the variance was unity. () Performance of the model, quantified by explained variance for populations of relay cells (n = 32) and interneurons (n = 5). Error bars represent s.d. * Figure 3: Quantitative comparison of postsynaptic currents recorded from all cells. (,) Examples of membrane currents characteristic of relay cells in black () and interneurons in blue () recorded in response to Gaussian noise (top) or natural movies (bottom) for four different cells. () Normalized deflection indices plotted against time scale for the whole dataset (n = 119); darker, thicker curves are from neurons illustrated in and . () Histogram plotting the distribution of the first principal component of the deflection index measured for each cell. * Figure 4: Voltage dependence of postsynaptic potentials recorded from relay cells and interneurons. (,) Spontaneous inputs to a relay cell () and interneuron () recorded while different amounts of current were injected, as indicated at left. () Averaged amplitudes of PSPs as a function of current injection, normalized to PSP amplitude at rest for three relay cells and three interneurons. Error bars indicate the s.d. Darker lines indicate responses shown in and . Records from relay cells are in black and from interneurons in blue. * Figure 5: Visual modulation of synaptic inputs to relay cells and interneurons. (,) Responses to disks of the preferred and anti-preferred contrast flashed in the centers of the receptive field of an OFF-center relay cell (, black) and interneuron (, blue). The dendritic arbors of each cell are drawn above responses to two individual presentations of the stimulus (darker colors) and the average for all trials (lighter colors). Gray bars indicate stimulus duration. * Figure 6: Rates of unitary synaptic events recorded from relay cells and interneurons. (,) Responses to disks of the preferred () and anti-preferred () contrast flashed in the center of the receptive field of a relay cell (example trial, black; variance across trials, gray) shown above histograms of EPSC rates for the same (black) and four additional relay cells (different colors). Bin size is 5 ms. Inset shows a segment of the recording at an expanded timescale and doubled gain to reveal differently sized EPSCs. (,) Companion responses and plots of event rate for five interneurons. * Figure 7: Spatial distribution of relay cells and interneurons. (–) Spatial distribution of the receptive fields of relay cells (,) and interneurons (,) shown as polar plots (,) and frequency histograms (,). a.c., area centralis. * Figure 8: Simulations of information transmitted by circuits that use different forms of synaptic integration. () Inputs to an exponential leaky integrate-and-fire model of a thalamic relay cell. A pink (1/f) noise signal (left) is transformed by complementary exponential functions to yield antagonistic (push-pull) firing rates (middle) for excitatory (ganglion cell, red) and inhibitory (interneuron, blue) inputs; input spike trains (right, rastergrams showing 100 trials) were generated as inhomogeneous Poisson processes. () Simulated membrane potential for control (black), altered excitation (green) and altered inhibition (purple). Dark-colored traces depict a single trial with nine additional trials in light gray. () Rastergrams for 100 trials depict spikes of the modeled relay cell for the three cases. Scale bar applies to and . () Information rate density plotted as a function of timescale for the three cases. The ordinate represents the negative derivative of the information rate, I(τ), with respect to τ. () Estimated information rate for the three cases. Error bars in represe! nt s.d. Author information * Abstract * Author information * Supplementary information Affiliations * Department of Biological Sciences and Neuroscience Graduate Program, University of Southern California, Los Angeles, California, USA. * Xin Wang, * Vishal Vaingankar, * Cristina Soto Sanchez & * Judith A Hirsch * Redwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, California, USA. * Friedrich T Sommer * Present address: Computational Neurobiology Laboratory, The Salk Institute for Biological Sciences, La Jolla, California, USA. * Xin Wang Contributions X.W. and J.A.H. performed the experiments with help from V.V. and C.S.S. X.W., J.A.H. and F.T.S. contributed to various analyses, and X.W. and F.T.S. developed the simulations. X.W., J.A.H. and F.T.S. wrote the manuscript, and X.W. prepared all of the figures. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Judith A Hirsch Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (488K) Supplementary Figures 1–4 Additional data
• Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex
  - Nat Neurosci 14(2):232-238 (2011)
  Nature Neuroscience | Article Computational and Systems Neuroscience Focus issue: February 2011 Volume 14, No 2 * * Reviews * Articles * Technical Report * * Contents * Editorial * Perspectives Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex * Jianzhong Jin1 Search for this author in: * NPG journals * PubMed * Google Scholar * Yushi Wang1 Search for this author in: * NPG journals * PubMed * Google Scholar * Harvey A Swadlow1, 2, 1 Search for this author in: * NPG journals * PubMed * Google Scholar * Jose M Alonso1, 2 Contact Jose M Alonso Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:232–238Year published:(2011)DOI:doi:10.1038/nn.2729Received20 July 2010Accepted30 November 2010Published online09 January 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg The primary visual cortex of primates and carnivores is organized into columns of neurons with similar preferences for stimulus orientation, but the developmental origin and function of this organization are still matters of debate. We found that the orientation preference of a cortical column is closely related to the population receptive field of its ON and OFF thalamic inputs. The receptive field scatter from the thalamic inputs was more limited than previously thought and matched the average receptive field size of neurons at the input layers of cortex. Moreover, the thalamic population receptive field (calculated as ON – OFF average) had separate ON and OFF subregions, similar to cortical neurons in layer 4, and provided an accurate prediction of the preferred orientation of the column. These results support developmental models of orientation maps that are based on the receptive field arrangement of ON and OFF visual inputs to cortex. View full text Figures at a glance * Figure 1: Geniculate afferents making monosynaptic connections with a cortical orientation column were identified by STCSD. () Recording method. () Spike-triggered local field potentials (STLFPs) and spike-triggered current source densities (STCSDs) generated by a single geniculate axon through the depth of the cortex. () Two afferents passing through an orientation column but only one (left) making a monosynaptic connection. () Cortical layers were identified with CSD analysis by stimulating the cortex with a full-field flash (see Online Methods). The orientation tuning was measured through the depth of the cortical column from multiunit activity responses to sweeping bars. () Example of simultaneously recorded geniculate afferents making monosynaptic connections with the same orientation column at different depths of layer 4. The afferents had different axonal conduction times, receptive field sizes and positions and receptive field response latencies. () Multiple recordings within a 200-μm cylinder of LGN allowed us to sample populations of geniculate afferents that made monosynaptic connecti! ons with the same orientation column. * Figure 2: The ON – OFF population receptive field of the geniculate inputs predicts the preferred orientation of the cortical column. () Strength of the current sinks generated by ON (red) and OFF (blue) geniculate afferents in two cortical orientation columns (top and bottom). () Receptive field coverage of the geniculate afferents that make monosynaptic connections with each column. () Superimposed ON and OFF receptive fields from the geniculate afferents. () Population receptive field calculated as ON – OFF average. () Orientation preference from multiunit activity recorded in cortical layer 4. The inset (top left) illustrates the difference between the orientation preference measured in cortex (continuous line) and predicted from the LGN population (LGNp, discontinuous line). AC, area centralis. * Figure 3: The ON – OFF population receptive field from connected geniculate inputs provides the most accurate prediction of cortical orientation preference. (,) Orientation predictions obtained with population receptive fields of monosynaptically connected geniculate neurons, calculated as an ON – OFF subtraction () or an ON + OFF sum that disregards contrast polarity (). Each panel quantifies the relationship between measured and predicted cortical orientation preference through linear correlation (top) and distribution of orientation differences (bottom). Examples of population receptive fields are shown around each scatter plot (red: ON, blue: OFF, purple: ON + OFF). () Orientation predictions from ON – OFF population receptive fields from geniculate neurons that were not connected but were retinotopically aligned with the cortical recordings. () ON – OFF population receptive fields from all geniculate neurons, connected and not connected. () Examples of population thalamic receptive fields shown in more detail as color maps. The population thalamic receptive field for each column is shown as ON – OFF (contour plot an! d top color map), ON + OFF (second row of color maps, starting from the top), ON sum (third row of color maps) and OFF sum (fourth row of color maps). The lines illustrate the 20% contour for the ON sum (continuous, red) and OFF sum (dotted, blue). * Figure 4: The orientation prediction from the ON – OFF population receptive field of geniculate inputs is highly significant and accurate, as demonstrated by Monte Carlo simulations. (,) Distributions of R2 () and orientation differences () calculated by comparing measurements of cortical orientation preference with predictions from shuffled population receptive fields of the geniculate inputs. The arrows show the R2 values and average orientation differences for non-shuffled population receptive fields. The ON – OFF population receptive field of connected geniculate afferents provides the largest R2 value and smallest orientation difference. The peak of the orientation-difference distribution is determined by the average and s.d. of the 10 orientation preferences measured in cortex. * Figure 5: The probability that two geniculate cells will make monosynaptic connections with the same orientation column is exponentially related to the distance between the geniculate receptive fields. (,) Exponential functions measured for the afferents to two orientation columns (in two different animals). () Exponential function measured in the entire geniculate cell population. Receptive field distance was measured using the diameter of the largest geniculate center within each pair as a unit. Inset: 95% of geniculate cell pairs had receptive fields separated by ≤1.5 geniculate centers and covered an area of visual space of 2.5 geniculate centers. * Figure 6: The ratio of ON/OFF afferents within each orientation column was correlated with the ratio of ON/OFF afferent strength, estimated by STCSD. That is, in columns where OFF afferents were more numerous, the OFF afferents also generated stronger current sinks than the ON afferents. This finding is consistent with previous studies18, 29, 30 indicating that ON and OFF afferents cluster in different cortical domains (right). Author information * Abstract * Author information * Supplementary information Affiliations * Department of Biological Sciences, State University of New York, College of Optometry, New York, New York, USA. * Jianzhong Jin, * Yushi Wang, * Harvey A Swadlow & * Jose M Alonso * Department of Psychology, University of Connecticut, Storrs, Connecticut, USA. * Harvey A Swadlow & * Jose M Alonso Contributions J.J., Y.W. and J.M.A. performed the experiments, J.J., Y.W., J.M.A. and H.A.S. were involved in data analysis, and J.M.A., H.A.S., J.J. and Y.W. wrote the paper. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Jose M Alonso Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (172K) Supplementary Figures 1–3 Additional data
• Decoding the activity of neuronal populations in macaque primary visual cortex
  - Nat Neurosci 14(2):239-245 (2011)
  Nature Neuroscience | Article Computational and Systems Neuroscience Focus issue: February 2011 Volume 14, No 2 * * Reviews * Articles * Technical Report * * Contents * Editorial * Perspectives Decoding the activity of neuronal populations in macaque primary visual cortex * Arnulf B A Graf1, 2 Contact Arnulf B A Graf Search for this author in: * NPG journals * PubMed * Google Scholar * Adam Kohn1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * Mehrdad Jazayeri1, 2 Search for this author in: * NPG journals * PubMed * Google Scholar * J Anthony Movshon1 Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:239–245Year published:(2011)DOI:doi:10.1038/nn.2733Received27 September 2010Accepted14 December 2010Published online09 January 2011Corrected online16 January 2011 Abstract * Abstract * Change history * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Visual function depends on the accuracy of signals carried by visual cortical neurons. Combining information across neurons should improve this accuracy because single neuron activity is variable. We examined the reliability of information inferred from populations of simultaneously recorded neurons in macaque primary visual cortex. We considered a decoding framework that computes the likelihood of visual stimuli from a pattern of population activity by linearly combining neuronal responses and tested this framework for orientation estimation and discrimination. We derived a simple parametric decoder assuming neuronal independence and a more sophisticated empirical decoder that learned the structure of the measured neuronal response distributions, including their correlated variability. The empirical decoder used the structure of these response distributions to perform better than its parametric variant, indicating that their structure contains critical information for senso! ry decoding. These results show how neuronal responses can best be used to inform perceptual decision-making. View full text Figures at a glance * Figure 1: Orientation tuning curves (spike count, mean ± s.e.m.) for a population of 60 neurons (data set 3). * Figure 2: Scheme of the linear log-likelihood decoding framework with parameters of the ELD derived from data set 3. To emphasize how the log-likelihood function is constructed, we ordered neurons by preferred orientations and averaged the neuronal response across trials and stimulus orientations to avoid distractions resulting from the different orientation responses. The average population activity was replicated for five stimulus orientations (bottom). A given stimulus elicited the population response represented by the black dots, each dot corresponding to the response of one neuron. We computed the average neuronal pooling weight across orientations and replicated it for three orientations, one of which coincided with the stimulus (middle). The population response corresponding to a stimulus (dots in lower panel) was combined with the pooling weights for each orientation (three colored curves in middle panel) to yield the log-likelihood function evaluated at these orientations (three colored dots in upper panel). The pooling weights can be seen as the coefficients of filters that dete! rmine how a neuron's response contributes to the log-likelihood; a neuron's contribution was positive when the stimulus was close to this neuron's preferred orientation, and was negative for remote stimuli. * Figure 3: Orientation estimation accuracy for the ELD, the CB-ELD and the PID. () The orientation estimate corresponding to a given neuronal population response was the orientation maximizing the log-likelihood function computed from this response. The estimation error is the difference between the estimated and true orientations. The absolute value of the overall estimation error (mean ± s.e.m.) for data set 3 was 0.77 ± 0.05, 2.21 ± 0.06 and 2.27 ± 0.06 degrees for the ELD, CB-ELD and PID, respectively. To describe estimation in more detail, we represented the distribution of estimation errors (mean ± s.e.m. estimated by bootstrap). The proportion of orientation estimates at the veridical stimulus orientation was 0.90 ± 0.00, 0.64 ± 0.01 and 0.62 ± 0.01 degrees for the ELD, CB-ELD and PID, respectively. () We compared data sets using the proportion of veridical estimates (mean ± s.e.m. estimated by bootstrap). * Figure 4: Orientation discrimination accuracy for the ELD, the CB-ELD and the PID. () The population neurometric functions represent the discrimination accuracy (mean ± s.e.m. across orientations) as function of the orientation difference. The interpolations were done using a cumulative Weibull distribution fitted using maximum likelihood. To avoid showing a neurometric function that mainly covers the asymptotic regime (accuracies close to 1), we averaged the discrimination accuracies across random subsets of 20 neurons from data set 3. The orientation discrimination threshold yielding an accuracy of 0.75 (mean ± s.e.m. estimated by bootstrap) was 2.58 ± 0.16, 3.70 ± 0.17 and 5.99 ± 0.20 degrees for the ELD, CB-ELD and PID, respectively. () We evaluated the discrimination accuracy across data sets by comparing their orientation discrimination thresholds (mean ± s.e.m. estimated by bootstrap) computed using entire populations. * Figure 5: Dependency of the discrimination weighting functions with respect to stimulus orientation and neuronal response magnitude for data set 3. () The discrimination weights were averaged across neurons after being aligned to each of the 72 possible discrimination boundaries. Neuronal preferred orientations were estimated from a fit to a von Mises function. The weights (mean ± s.e.m.) of the ELD are shown for fine (dark) and coarse (light) discriminations. (,) Data are presented as in , but for the CB-ELD and the PID. () The absolute value of the discrimination weights of the ELD (mean ± s.e.m.) is plotted against the neuronal response strength (spike counts) across all orientations by ordering for each neuron the discrimination weights by increasing neuronal response to one of the two discriminated orientations. We plotted their running average using a window over the 100 nearest neighbors for fine (dark) and coarse (light) discriminations. (,) Data are presented as in , but for the CB-ELD and the PID. * Figure 6: Orientation discrimination thresholds of the ELD corresponding to an accuracy of 0.75 using data set 3. The discrimination thresholds were derived from the neurometric functions evaluated at each stimulus orientation. We computed the discrimination thresholds of the entire population of 60 neurons (gray line) and for each neuron individually (all dots, each corresponding to the threshold of one neuron). We then chose a random subset of 10 neurons and computed the corresponding population threshold (black line, the black dots indicating the corresponding individual thresholds). Change history * Abstract * Change history * Author information * Supplementary informationErratum 16 January 2011In the version of this article initially published online, an error was made in the legend for Figure 6. In the legend, 0.75% should read 0.75. This error has been corrected for the print, PDF and HTML versions of this article. Author information * Abstract * Change history * Author information * Supplementary information Affiliations * Center for Neural Science, New York University, New York, New York, USA. * Arnulf B A Graf, * Adam Kohn, * Mehrdad Jazayeri & * J Anthony Movshon * Present address: Division of Biology, California Institute of Technology, Pasadena, California, USA (A.B.A.G.), Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York, USA (A.K.), National Primate Research Center and Department of Physiology and Biophysics, University of Washington, Seattle, Washington, USA (M.J.). * Arnulf B A Graf, * Adam Kohn & * Mehrdad Jazayeri Contributions A.B.A.G., A.K. and J.A.M. designed the experiments, A.B.A.G. and A.K. collected the data, A.B.A.G. created the models and analyzed the data, A.B.A.G. and J.A.M. wrote the manuscript, and A.K. and M.J. contributed to the intellectual development of the project and to the writing of the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Arnulf B A Graf Supplementary information * Abstract * Change history * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–8 Additional data
• The auditory cortex mediates the perceptual effects of acoustic temporal expectation
  - Nat Neurosci 14(2):246-251 (2011)
  Nature Neuroscience | Article Computational and Systems Neuroscience Focus issue: February 2011 Volume 14, No 2 * * Reviews * Articles * Technical Report * * Contents * Editorial * Perspectives The auditory cortex mediates the perceptual effects of acoustic temporal expectation * Santiago Jaramillo1 Search for this author in: * NPG journals * PubMed * Google Scholar * Anthony M Zador1 Contact Anthony M Zador Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorJournal name:Nature NeuroscienceVolume: 14,Pages:246–251Year published:(2011)DOI:doi:10.1038/nn.2688Received11 May 2010Accepted12 October 2010Published online19 December 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg When events occur at predictable instants, anticipation improves performance. Knowledge of event timing modulates motor circuits and thereby improves response speed. By contrast, the neuronal mechanisms that underlie changes in sensory perception resulting from expectation are not well understood. We developed a behavioral procedure for rats in which we manipulated expectations about sound timing. Valid expectations improved both the speed and the accuracy of the subjects' performance, indicating not only improved motor preparedness but also enhanced perception. Single-neuron recordings in primary auditory cortex showed enhanced representation of sounds during periods of heightened expectation. Furthermore, we found that activity in auditory cortex was causally linked to the performance of the task and that changes in the neuronal representation of sounds predicted performance on a trial-by-trial basis. Our results indicate that changes in neuronal representation as early as! primary sensory cortex mediate the perceptual advantage conferred by temporal expectation. View full text Figures at a glance * Figure 1: Task and manipulation of temporal expectation. () Rats initiated each trial by a nose poke into the center port of the operant chamber12, 36. After a variable (250–350 ms) silent period, a stimulus consisting of a frequency-modulated target in a train of pure tone distractors was presented. Rats were required to stay in the port until the target was presented. The center frequency of the target (6.5 kHz or 31 kHz) indicated the side port where water reward would be delivered on each trial (left or right, respectively). () The stimulus consisted of a sequence of 100-ms pure tones (5–40 kHz) separated by 50 ms, which was presented for as long as the animal stayed in the port. The frequency-modulated target was presented in place of one of the tones in each trial. () Temporal expectation was manipulated by changing the ratio of trials with early or late targets within each block of 150–200 trials. * Figure 2: Valid temporal expectation improved performance. () Behavioral responses were faster on trials with expected targets. Example of the reaction time distribution from one rat at the easiest difficulty tested (TMD ≈ 25%) for early targets that were expected (blue) or unexpected (green). Reaction time was defined as the time between the onset of a target and the moment when the rat left the center port. () Median reaction time for each rat (dots) on the easiest difficulty tested, and average across all eight rats for early targets that were expected (blue) or unexpected (green). () Behavioral responses were more accurate on trials with expected targets. Example for one rat of the percentage of correct trials as a function of difficulty, varied here by the modulation depth of the target (TMD). Error bars correspond to the 95% confidence intervals on estimates. The dashed line corresponds to 75% performance used for calculating thresholds in . () Modulation depth needed to achieve 75% correct trials for each rat (dots) and ave! rage across all eight rats (colored bars). **P < 0.01. * Figure 3: Inactivation of auditory cortex decreased performance. () Bilateral reversible inactivation of auditory cortex (AC) was performed by applying the GABAA receptor agonist muscimol to the surface of the exposed dura mater. Craniotomies were protected by implanted wells. Darker- and lighter-colored regions indicate primary and secondary auditory cortices, respectively37. () Performance on expected early targets as a function of difficulty on interleaved inactivation (gray) and control (black) sessions. The plot shows mean ± s.e.m. for five rats. * Figure 4: Temporal expectation modulated neuronal activity in the auditory cortex. () Responses of a single neuron to the same sequence of tones under two temporal expectation conditions: expecting an early (blue) or a late (red) target. Expected early targets appeared after 450 ms, whereas expected late targets (not visible here) were presented after 1,500 ms. Trials are aligned to the onset of the first tone (gray vertical line) for the spike raster (top) and the peristimulus time histogram for each condition (bottom). The session included more than two blocks of trials, but all expect-early or expect-late blocks are grouped together here for illustration. The frequency and duration of each tone are indicated by the gray boxes. The greatest difference in evoked activity is seen for the tone that immediately preceded the early target. The stimuli presented after 450 ms are not the same on each trial; average responses for these stimuli are shown as dashed lines. () Modulation index of 44 responsive cells recorded during sessions in which all tones that pr! eceded the early targets had fixed frequencies. A positive modulation index indicates a stronger response on expect-early trials. Cells with statistically significant modulation (P < 0.05, Wilcoxon rank-sum test) are shown in black. The gray triangle indicates the mean of the modulation index. The white triangle shows the modulation index for the example in . () Evoked local field potential (mean ± s.e.m.) at one recording site. The onset of the early target is indicated by the blue triangle. () Modulation of local field potentials. Colors as in . * Figure 5: Modulation of neuronal activity was specific to driven activity. () Frequency tuning of a single cell, estimated from responses to the third tone in each expectation condition (Supplementary Fig. 10). Each point represents the mean ± s.e.m. firing rate for each tone frequency. *P < 0.05, ***P < 0.001, Wilcoxon rank-sum test. () Average frequency tuning of 58 cells recorded with a third tone of random frequency. Individual tuning curves were aligned according to the preferred frequency of each cell and normalized before averaging. Each point indicates the mean ± s.e.m. across neurons. * Figure 6: Neuronal response increased as the expected moment of the target approached. () Frequency tuning of cell in Figure 5a as the time of the late target approaches. For each time slot preceding the late target, the estimated tuning curve is plotted in a different color. The color bar shows the time of each time slot with respect to the target onset. () Neuronal response to each cell's preferred frequency (PF) as the time of the target approaches. Responses were normalized with respect to the response to the fourth tone (−1,050 ms from late target onset). Each point corresponds to the median across cells, with error bars proportional to the median absolute deviation. Only responses to the preferred frequency for each cell are shown, and only cells with responses above spontaneous firing for each time slot were included (n = 20). *P < 0.05, **P < 0.01 with respect to response to fourth tone; paired Wilcoxon signed-rank test. * Figure 7: Neuronal activity in auditory cortex was correlated with behavioral performance. () Average evoked LFP from a single electrode for trials with expected early targets grouped according to reaction time. Averages are taken over those trials with the 20% fastest (blue solid) or the 20% slowest (blue dashed) reaction times. Evoked LFP for trials with late targets is shown in red for comparison. The light-colored bands surrounding each trace indicate the s.e.m. across trials. The stimulus (third tone with fixed frequency across trials) is indicated by the gray bar, and the onset time of an early target is represented by the blue triangle. () Evoked LFPs were larger on trials with faster behavioral responses. Difference in evoked LFP magnitude between trials with the fastest and slowest behavioral responses. The difference is quantified by a modulation index between the average response on fast (LFPF) and slow (LFPS) trials for each recording site (n = 59). Sites with a significant difference (P < 0.05, permutation test) are shown in black. The gray triangle i! ndicates the mean modulation index. () Evoked spiking activity was higher on trials with faster behavioral responses. Difference in evoked activity on single cells between trials with the fastest and slowest behavioral responses. The difference is quantified by a modulation index between the average response on fast (RF) and slow (RS) trials for each cell (n = 44). Cells with a significant difference (P < 0.05, Wilcoxon rank-sum test) are shown in black. The gray triangle indicates the mean modulation index. Only sessions in which the third tone had the same frequency on all trials were included in this analysis. Author information * Abstract * Author information * Supplementary information Affiliations * Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA. * Santiago Jaramillo & * Anthony M Zador Contributions S.J. and A.M.Z. designed the project and wrote the manuscript. S.J. collected the data and performed the analyses. Competing financial interests The authors declare no competing financial interests. Corresponding author Correspondence to: * Anthony M Zador Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (512K) Supplementary Figures 1–14 and Supplementary Equations 1–3 Additional data
• Predictive remapping of attention across eye movements
  - Nat Neurosci 14(2):252-256 (2011)
  Nature Neuroscience | Article Predictive remapping of attention across eye movements * Martin Rolfs1, 2 Contact Martin Rolfs Search for this author in: * NPG journals * PubMed * Google Scholar * Donatas Jonikaitis3 Search for this author in: * NPG journals * PubMed * Google Scholar * Heiner Deubel3 Search for this author in: * NPG journals * PubMed * Google Scholar * Patrick Cavanagh1 Contact Patrick Cavanagh Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature NeuroscienceVolume: 14,Pages:252–256Year published:(2011)DOI:doi:10.1038/nn.2711Received24 September 2010Accepted08 November 2010Published online26 December 2010 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Many cells in retinotopic brain areas increase their activity when saccades (rapid eye movements) are about to bring stimuli into their receptive fields. Although previous work has attempted to look at the functional correlates of such predictive remapping, no study has explicitly tested for better attentional performance at the future retinal locations of attended targets. We found that, briefly before the eyes start moving, attention drawn to the targets of upcoming saccades also shifted to those retinal locations that the targets would cover once the eyes had moved, facilitating future movements. This suggests that presaccadic visual attention shifts serve to both improve presaccadic perceptual processing at the target locations and speed subsequent eye movements to their new postsaccadic locations. Predictive remapping of attention provides a sparse, efficient mechanism for keeping track of relevant parts of the scene when frequent rapid eye movements provoke retinal sme! ar and temporal masking. View full text Figures at a glance * Figure 1: Predictive remapping across eye movements. () If two saccades are planned, first from the red to the blue kite and then to the kite handles visible near the surfer's left elbow, the second target (red circle) is attended in parallel to the first9, 10, 11. Remapping triggers a predictive activation of cells responding to the future retinotopic location of the second target, offset from its current location in the direction opposite the saccade vector (black circle)16. We found that this predictive activation was accompanied by an attention shift to that retinotopic location, specifying the location for the subsequent saccade. () The functional direction of remapping. Two previous studies have targeted behavioral correlates of remapping18, 19, but actually examined a reversal of remapping that has no functional correlate (see also Supplementary Fig. 1). In these studies, the effect of a spatial cue18 (or, equivalently, an adaptor19) on subsequent pre-saccadic tests was assessed at a location offset from the cue locatio! n in the same direction as the saccade vector (middle left). This location is the opposite of the actual remapped location (middle right) and corresponds instead to the future world-centered location of the cue's current retinal location. After the saccade, this reversed remapped location covers retinotopic cortex that is far from the spatial location of the cue. * Figure 2: Predictive remapping of attention in the double-step task. () Stimulus layout. Six stimuli, arranged in a regular hexagon, displayed a flickering stream of grating-mask pairs. Following a central movement cue, subjects quickly made two eye movements, the first one left or right (here, right), the second one up or down (here, up). One of the six gratings changed orientation (probe stream; here at remapped location) 50–400 ms after the movement cue, whereas all others remained vertical (distractor streams). After the eye movements, subjects reported the direction of tilt that they had seen (\ or /), regardless of its location. Using performance in this task, we measured the deployment of attention at four locations (dashed frames) during the latency of the first saccade. () Performance as a function of probe offset relative to the saccade, superimposed for the probe locations tested. Error bars represent s.e.m. * Figure 3: Controlling for the spread of attention in the double-step task. We repeated the double-step task in a new set of subjects, probing the location adjacent to the first saccade target to test whether attentional benefits extend around saccade targets, an alternative interpretation of the effect at the remapped location. Performance is shown as a function of probe offset relative to the saccade. Attention did not spread around saccade targets. Instead, it shifted specifically to the remapped location of the second saccade target. Error bars represent s.e.m. * Figure 4: Controlling for cue-based facilitation in the double-step task. () In this version of the double-step task, we used only one cue, excluding the possibility of a cue-based attentional facilitation at the remapped location. The single cue indicated the first saccade target (any of the six; here upper right); the second saccade target was always the next stimulus in the clockwise direction (here, right). We measured the deployment of attention at four locations (dashed frames) during the latency of the first saccade. Testing the location adjacent to the first saccade target, this experiment also again tested whether attentional benefits extend around saccade targets, an alternative interpretation of the effect at the remapped location (see also Fig. 3). () Performance as a function of probe offset relative to the saccade. Again, briefly before the saccade, attention shifted specifically to the remapped location of the second saccade target. Error bars represent s.e.m. * Figure 5: Predictive remapping of attention to the fovea. () Stimulus layout in the single-step task. We presented three stimuli, arranged at equal distances in a line. Otherwise the procedure was identical to that in the double-step task (Fig. 2a). Following a movement cue (here, right), subjects quickly made an eye movement to the indicated target and reported the direction of the tilted stimulus, regardless of its location. () Performance at the probed locations as a function of probe offset relative to the saccade. Error bars represent s.e.m. Author information * Abstract * Author information * Supplementary information Affiliations * Université Paris Descartes, Laboratoire Psychologie de la Perception, Paris, France. * Martin Rolfs & * Patrick Cavanagh * New York University, Department of Psychology, New York, New York, USA. * Martin Rolfs * Ludwig-Maximilians-Universität, Department Psychologie, München, Germany. * Donatas Jonikaitis & * Heiner Deubel Contributions M.R., D.J., H.D. and P.C. designed the experiments. M.R. and D.J. conducted the experiments and analyzed the data. M.R. and P.C. wrote the manuscript. P.C. and H.D. supervised the project. All of the authors discussed the results and commented on the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Martin Rolfs or * Patrick Cavanagh Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (1M) Supplementary Figures 1–5 Additional data
• Anatomically distinct dopamine release during anticipation and experience of peak emotion to music
  - Nat Neurosci 14(2):257-262 (2011)
  Nature Neuroscience | Article Anatomically distinct dopamine release during anticipation and experience of peak emotion to music * Valorie N Salimpoor1, 2, 3 Contact Valorie N Salimpoor Search for this author in: * NPG journals * PubMed * Google Scholar * Mitchel Benovoy3, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Kevin Larcher1 Search for this author in: * NPG journals * PubMed * Google Scholar * Alain Dagher1 Search for this author in: * NPG journals * PubMed * Google Scholar * Robert J Zatorre1, 2, 3 Contact Robert J Zatorre Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature NeuroscienceVolume: 14,Pages:257–262Year published:(2011)DOI:doi:10.1038/nn.2726Received07 October 2010Accepted25 November 2010Published online09 January 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Music, an abstract stimulus, can arouse feelings of euphoria and craving, similar to tangible rewards that involve the striatal dopaminergic system. Using the neurochemical specificity of [11C]raclopride positron emission tomography scanning, combined with psychophysiological measures of autonomic nervous system activity, we found endogenous dopamine release in the striatum at peak emotional arousal during music listening. To examine the time course of dopamine release, we used functional magnetic resonance imaging with the same stimuli and listeners, and found a functional dissociation: the caudate was more involved during the anticipation and the nucleus accumbens was more involved during the experience of peak emotional responses to music. These results indicate that intense pleasure in response to music can lead to dopamine release in the striatal system. Notably, the anticipation of an abstract reward can result in dopamine release in an anatomical pathway distinct from! that associated with the peak pleasure itself. Our results help to explain why music is of such high value across all human societies. View full text Figures at a glance * Figure 1: Positive correlation between emotional arousal and intensity of chills during PET scanning. The mean intensity of chills reported by each participant during the PET scanning session was significantly correlated with psychophysiological measurements that were also acquired during the scan. These are indicative of increased sympathetic nervous system activity, suggesting that the intensity of chills is a good marker of peak emotional arousal (Supplementary Table 1). The y axis represents standardized z scores for each biosignal. See main text for P-values. * Figure 2: Evidence for dopamine release during pleasurable music listening. () Statistical parametric maps (t statistic on sagittal, coronal and axial slices) reveal significant (P < 0.001) [11C]raclopride binding potential (BP) decreases bilaterally in the caudate, putamen and NAcc (white arrows) during pleasurable compared with neutral music listening (Supplementary Table 2), indicating increased dopamine release during pleasurable music. () Changes in binding potential (BP) values plotted separately for each individual; note that the change was consistent for the majority of people at each site. * Figure 3: Combined fMRI and PET results reveal temporal distinctions in regions showing dopamine release. () [11C]raclopride PET scan results were spatially conjoined with the fMRI results by creating a mask of significant dopamine release overlayed on BOLD response t maps during each condition. () Hemodynamic responses and dopamine activity were maximal in the caudate during anticipatory phases, but shifted more ventrally to NAcc during peak emotional responses. () Percent signal change in BOLD response relative to the mean was calculated from the peak voxel of the caudate and NAcc clusters based on the [11C]raclopride PET data. Voxels showing maximum dopamine release in the caudate and NAcc (Supplementary Table 2) were identified and percent BOLD signal change was calculated during the fMRI epochs associated with peak emotional responses; values were interpolated for each second preceding this response for each individual, up to 15 s, which was defined as the anticipatory period based on previous findings15 (see Online Methods for additional details). We found increased activi! ty during anticipation (A1-A15) and decreased activity during peak emotional response (C1-C4) for the caudate, but a continuous increase in activity in NAcc with a maximum during peak emotional responses. The mean signal for neutral epochs for the NAcc and caudate clusters are also plotted for reference, as are the 5 s preceding the anticipation epochs. * Figure 4: Brain and behavior relationships involving temporal components of pleasure during music listening. Left, coronal slices showing binding potential differences in dorsal (top) and ventral (bottom) striatum that also show hemodynamic activity during anticipation versus experience of chills, respectively. Right, behavioral ratings of the number and intensity of chills and pleasure reported during the PET scans plotted against [11C]raclopride binding potential changes in the two clusters. The number of chills reported was positively correlated with percent binding potential change in the caudate (*P < 0.05), which was linked to BOLD response immediately preceding chills (that is, anticipatory periods), consistent with the idea that a greater number of chills would result in greater anticipation and result in more activity in the areas associated with anticipation. The mean intensity of chills and reported pleasure were positively correlated with the NAcc (**P < 0.01), which was linked to BOLD response during chills, confirming that this region is involved in the experience of ! the highly pleasurable component of music listening. * Figure 5: Brain and behavior relationships involving parametric increases in pleasure during music listening. Relationship between real-time ratings of pleasure during music listening and percent BOLD signal change relative to the mean in regions showing dopamine release as identified via PET. The chills epochs (shaded) were excluded from the analysis (values shown here only for reference) to examine activity related to increases in pleasure irrespective of chills. A regression analysis revealed that the NAcc, and to a lesser extent the left and right caudate, significantly predicted increases in pleasure ratings during each of the conditions (P < 0.05 and P < 0.001, respectively; Supplementary Table 4). This analysis indicates that activity in these regions increased with pleasure even when no chills were experienced.LP, low pleasure; HP, high pleasure. Author information * Abstract * Author information * Supplementary information Affiliations * Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada. * Valorie N Salimpoor, * Kevin Larcher, * Alain Dagher & * Robert J Zatorre * International Laboratory for Brain, Music and Sound Research, Montreal, Quebec, Canada. * Valorie N Salimpoor & * Robert J Zatorre * Centre for Interdisciplinary Research in Music Media and Technology, Montreal, Quebec, Canada. * Valorie N Salimpoor, * Mitchel Benovoy & * Robert J Zatorre * Centre for Intelligent Machines, McGill University, Montreal, Quebec, Canada. * Mitchel Benovoy Contributions V.N.S., R.J.Z. and A.D. designed the study. V.N.S. and M.B. performed all experiments. V.N.S., M.B. and K.L. analyzed the data. V.N.S. and R.J.Z. wrote the manuscript. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Valorie N Salimpoor or * Robert J Zatorre Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (2M) Supplementary Figures 1–3 and Supplementary Tables 1–4 Additional data
• A wireless multi-channel neural amplifier for freely moving animals
  - Nat Neurosci 14(2):263-269 (2011)
  Nature Neuroscience | Technical Report Computational and Systems Neuroscience Focus issue: February 2011 Volume 14, No 2 * * Reviews * Articles * Technical Report * * Contents * Editorial * Perspectives A wireless multi-channel neural amplifier for freely moving animals * Tobi A Szuts1 Contact Tobi A Szuts Search for this author in: * NPG journals * PubMed * Google Scholar * Vitaliy Fadeyev2 Search for this author in: * NPG journals * PubMed * Google Scholar * Sergei Kachiguine2 Search for this author in: * NPG journals * PubMed * Google Scholar * Alexander Sher2 Search for this author in: * NPG journals * PubMed * Google Scholar * Matthew V Grivich2 Search for this author in: * NPG journals * PubMed * Google Scholar * Margarida Agrochão3, 4 Search for this author in: * NPG journals * PubMed * Google Scholar * Pawel Hottowy5 Search for this author in: * NPG journals * PubMed * Google Scholar * Wladyslaw Dabrowski5 Search for this author in: * NPG journals * PubMed * Google Scholar * Evgueniy V Lubenov6 Search for this author in: * NPG journals * PubMed * Google Scholar * Athanassios G Siapas6 Search for this author in: * NPG journals * PubMed * Google Scholar * Naoshige Uchida3, 7 Search for this author in: * NPG journals * PubMed * Google Scholar * Alan M Litke2 Search for this author in: * NPG journals * PubMed * Google Scholar * Markus Meister3, 7 Contact Markus Meister Search for this author in: * NPG journals * PubMed * Google Scholar * Affiliations * Contributions * Corresponding authorsJournal name:Nature NeuroscienceVolume: 14,Pages:263–269Year published:(2011)DOI:doi:10.1038/nn.2730Received21 October 2010Accepted06 December 2010Published online16 January 2011 Abstract * Abstract * Author information * Supplementary information Article tools * Full text * Print * Email * Download PDF * Download citation * Order reprints * Rights and permissions * Share/bookmark * Connotea * CiteULike * Facebook * Twitter * Delicious * Digg Conventional neural recording systems restrict behavioral experiments to a flat indoor environment compatible with the cable that tethers the subject to recording instruments. To overcome these constraints, we developed a wireless multi-channel system for recording neural signals from rats. The device takes up to 64 voltage signals from implanted electrodes, samples each at 20 kHz, time-division multiplexes them into one signal and transmits that output by radio frequency to a receiver up to 60 m away. The system introduces <4 μV of electrode-referred noise, comparable to wired recording systems, and outperforms existing rodent telemetry systems in channel count, weight and transmission range. This allows effective recording of brain signals in freely behaving animals. We report measurements of neural population activity taken outdoors and in tunnels. Neural firing in the visual cortex was relatively sparse, correlated even across large distances and was strongly influenced! by locomotor activity. View full text Figures at a glance * Figure 1: System overview, showing all components (bottom left) and the complete system worn by a rat (bottom right). Roman numerals indicate points of the signal path that are illustrated in Figure 3. A ground shield around the outer edge of the circular headstage circuit boards (bottom left) has been removed for clarity. * Figure 2: Schematic circuits. () Block diagram of the head board: the Neuroplat chip amplifies, filters and analog multiplexes up to 64 input signals. The multiplexed signal is combined with a synchronization or calibration pulse in an analog multiplexer chip, and then buffered by a line driver. A PLC provides command and control signals to the Neuroplat chip to generate the multiplexed output and to set the channel gain and passband. The PLC also provides the control signals to the multiplexer. A 10-MHz quartz oscillator supplies the PLC's clocking signals. () Block diagram of the 64-channel Neuroplat chip9. * Figure 3: Signal path, showing schematically how electrode voltages are transformed by the wireless system. Raw electrode voltages (I) are first multiplexed and combined with a synchronization pulse into 50-μs data fields (II). Transmission (III) introduces a highpass pre-emphasis filter. Signal decoding follows a reverse path: first convolution with an appropriate decoding filter, then demultiplexing and reconstruction of the electrode voltage by linear weighting over the relevant data points. * Figure 4: Noise and range measurements. () Voltage trace from extracellular recording in hippocampus before and after transmission (negative voltage plotted upward). (,) Shape separation of the spikes by cluster analysis in the space of waveforms. Clusters are shown with colors: the same PCA basis was used for both. () Electrode voltages recorded at distances of 1 m and 60 m, showing spikes from two different units. Traces were bandpass filtered 500–3,000 Hz. (,) Shape separation of the spikes shown in , color coded according to unit identity. The same PCA basis was used for both projections. Behavioral state was not controlled between the two conditions and probably accounts for the change in rate. () Normalized SNR as a function of transmission distance and antenna orientation, using a test signal. SNR is normalized to its value at 1 m. The receiver's stub antenna was perpendicular to the line-of-sight to the transmitter. The transmitter's antenna was either parallel to the receiver's or perpendicular, with re! spect to the line-of-sight path. The small dip at 30 m may derive from uncontrolled RF reflections in a cluttered environment. * Figure 5: Illustrative data: spike trains and response properties from the rat cortex. () Activity recorded from the premotor cortex during an odor discrimination task. Raster graph shows a single-unit spike train; each row is a single trial. Spike times (black ticks) are measured relative to when the rat left the central odor delivery port ('odor port out') after odor delivery. Colored ticks indicate when a side water port was reached ('water port in') and background shading indicates which water port the animal chose. The four odorants were combinations of caproic acid (odor A) and 1-hexanol (odor B) in the ratios indicated by colored bars to the right. When odor A was stronger, the water reward came from the left port and otherwise from the right port. Below, average firing rate analyzed by response port (right versus left) and odor stimulus (indicated by color), showing response only during leftward motion. (–) Neurons from visual cortex recorded in an indoor arena. () Response of neuron A to the illuminant flashing periodically at 4 Hz. Top, raster grap! h of spikes on successive stimulus repeats. Middle, average firing rate over all repeats. Bottom, light intensity. Right, number of spikes averaged over four consecutive repeats. Far right: motion score (1 if animal moved in a given 0.5 s period, 0 otherwise). Beginning at repetitions 450 and 850, the peak latency increased gradually from 40 to 70 ms. Both changes occurred at the start of a putative sleeping period (defined by lack of motion for >15 s). () Spike-triggered average stimulus derived from the spike trains of neurons A and B under random flicker of the illuminant. Top, sample stimulus trace of 20-Hz random flicker. Bottom, average intensity (from −0.5 to 0.5) plotted as a function of time preceding an action potential. () Autocorrelation functions for neurons A and B under random flicker stimulation, colored as in . The cell's average firing rate is plotted as a function of time since one of its own spikes. Neuron A was modulated at multiples of 50 ms, indicat! ing phase-locking to the flicker rate. * Figure 6: Population measures and behavioral modulation of V1 activity indoors. () Spike waveforms from neurons recorded in V1. The spike shapes were separated by principal component analysis (not shown) into two clusters. Bold lines are the average waveforms for the broad and narrow spike shapes. Negative voltage is plotted upwards. () Cross-correlation functions of three neuron pairs, indicating synchronized firing on different time scales. Results are plotted as the firing rate of one neuron against the delay from a spike of the other neuron. () Histogram of the correlation index for neuron pairs recorded from the same tetrode (top) and from different tetrodes (bottom). This measures the central peak of the cross-correlation function () by the ratio of the mean value in a 50-ms window around 0 s delay to the mean value around delays of 1 s and −1 s. The two distributions are not significantly different (n = 25 for same tetrode pairs, n = 201 for different; P = 0.2; Wilcoxon rank sum test). () Firing rate of five neurons (in 0.5-s bins, no smoothing! ) overlaid with the rat's speed, showing increased activity during locomotion. At the bottom, electrode traces are shown during the fastest and slowest motion episodes, to demonstrate lack of disruptive motion artifacts. The bottom trace is full bandwidth, the top trace is filtered 500–3,000 Hz. () Normalized cross-correlation functions (CCF) between neural firing rate (colored as in ) and the animal's speed. () Histogram of the peak height of the motion-spiking correlation function for 20 units. The peak height was measured relative to the average value at +10 and −10 s delay. * Figure 7: Behavioral modulation of activity in V1 outdoors. () Spike raster of two V1 neurons during digging behavior. Head up indicates when the eyes were above the leaf litter, head down when they were below. () Average firing rate as a function of head position for four neurons. The linear fit has a slope of 1.47 ± 0.13 (mean ± s.d.) and offset of 0.94 ± 0.69. Error bars are the s.d. of the firing rate for three repeated measurements of head position (scored manually from the video) and the uncertainty based on the number of spikes observed (using Poisson statistics): those not visible are smaller than the symbol. Colors as in . () Photograph of a rat carrying the wireless system as it emerges from the pipe. () Electrode voltages recorded while the animal was inside a pipe (top) or outside (bottom); each trace shown at full bandwidth and bandpass filtered 500–3,000 Hz. () Firing rate modulation of a V1 neuron (NSU) as the animal emerges from the pipe, on three separate occasions. Time zero is when the eyes traversed the end p! lane of the pipe. Author information * Abstract * Author information * Supplementary information Affiliations * Program in Biophysics, Harvard University, Cambridge, Massachusetts, USA. * Tobi A Szuts * Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, California, USA. * Vitaliy Fadeyev, * Sergei Kachiguine, * Alexander Sher, * Matthew V Grivich & * Alan M Litke * Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, USA. * Margarida Agrochão, * Naoshige Uchida & * Markus Meister * Champalimaud Neuroscience Programme, Instituto Gulbenkian de Ciencia, Portugal. * Margarida Agrochão * Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Krakow, Poland. * Pawel Hottowy & * Wladyslaw Dabrowski * Division of Biology, and Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, USA. * Evgueniy V Lubenov & * Athanassios G Siapas * Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA. * Naoshige Uchida & * Markus Meister Contributions This manuscript was written by T.A.S. and M.M., with comments from all authors. The Neuroplat chip was designed by P.H., W.D. and A.M.L. The back and head boards were designed by A.M.L., V.F., S.K., A.S. and M.V.G. The wireless link was designed by T.A.S. and M.M. Implantations and experiments were performed by A.G.S. and E.V.L. (hippocampus), N.U. (frontal eye field), and T.A.S. and M.A. (V1). Analysis was performed by N.U. (FEF) and T.A.S. and M.M. (V1, hippocampus). M.M. and A.M.L. supervised the project. Competing financial interests The authors declare no competing financial interests. Corresponding authors Correspondence to: * Tobi A Szuts or * Markus Meister Supplementary information * Abstract * Author information * Supplementary information PDF files * Supplementary Text and Figures (140K) Supplementary Table 1 Additional data