Friday, November 11, 2011

Hot off the presses! Nov 03 Neuron

The Nov 03 issue of the Neuron is now up on Pubget (About Neuron): if you're at a subscribing institution, just click the link in the latest link at the home page. (Note you'll only be able to get all the PDFs in the issue if your institution subscribes to Pubget.)

Latest Articles Include:

  • Motor Neurons and the Sense of Place
    - Neuron 72(3):419-424 (2011)
    Seventy years ago George Romanes began to document the anatomical organization of the spinal motor system, uncovering a multilayered topographic plan that links the clustering and settling position of motor neurons to the spatial arrangement and biomechanical features of limb muscles. To this day, these findings have provided a structural foundation for analysis of the neural control of movement and serve as a guide for studies to explore mechanisms that direct the wiring of spinal motor circuits. In this brief essay we outline the core of Romanes's findings and place them in the context of recent studies that begin to provide insight into molecular programs that assign motor pool position and to resolve how motor neuron position shapes circuit assembly. Romanes's findings reveal how and why neuronal positioning contributes to sensory-motor connectivity and may have relevance to circuit organization in other regions of the central nervous system.
  • Computational Mechanisms of Sensorimotor Control
    - Neuron 72(3):425-442 (2011)
    In order to generate skilled and efficient actions, the motor system must find solutions to several problems inherent in sensorimotor control, including nonlinearity, nonstationarity, delays, redundancy, uncertainty, and noise. We review these problems and five computational mechanisms that the brain may use to limit their deleterious effects: optimal feedback control, impedance control, predictive control, Bayesian decision theory, and sensorimotor learning. Together, these computational mechanisms allow skilled and fluent sensorimotor behavior.
  • Neuroplasticity Subserving Motor Skill Learning
    - Neuron 72(3):443-454 (2011)
    Recent years have seen significant progress in our understanding of the neural substrates of motor skill learning. Advances in neuroimaging provide new insight into functional reorganization associated with the acquisition, consolidation, and retention of motor skills. Plastic changes involving structural reorganization in gray and white matter architecture that occur over shorter time periods than previously thought have been documented as well. Data from experimental animals provided crucial information on plausible cellular and molecular substrates contributing to brain reorganization underlying skill acquisition in humans. Here, we review findings demonstrating functional and structural plasticity across different spatial and temporal scales that mediate motor skill learning while identifying converging areas of interest and possible avenues for future research.
  • Neuronal Basis for Object Location in the Vibrissa Scanning Sensorimotor System
    - Neuron 72(3):455-468 (2011)
    An essential issue in perception is how the location of an object is estimated from tactile signals in the context of self-generated changes in sensor configuration. Here, we review the pathways and dynamics of neuronal signals that encode touch in the rodent vibrissa sensorimotor system. Rodents rhythmically scan an array of long, facial hairs across a region of interest. Behavioral evidence shows that these animals maintain knowledge of the azimuthal position of their vibrissae. Electrophysiological measurements have identified a reafferent signal of the azimuth that is coded in normalized coordinates, broadcast throughout primary sensory cortex and provides strong modulation of signals of vibrissa contact. Efferent signals in motor cortex report the range of the scan. Collectively, these signals allow the rodent to form a percept of object location.
  • Are We Ready for a Natural History of Motor Learning?
    - Neuron 72(3):469-476 (2011)
    Here we argue that general principles with regard to the contributions of the cerebellum, basal ganglia, and primary motor cortex to motor learning can begin to be inferred from explicit comparison across model systems and consideration of phylogeny. Both the cerebellum and the basal ganglia have highly conserved circuit architecture in vertebrates. The cerebellum has consistently been shown to be necessary for adaptation of eye and limb movements. The precise contribution of the basal ganglia to motor learning remains unclear but one consistent finding is that they are necessary for early acquisition of novel sequential actions. The primary motor cortex allows independent control of joints and construction of new movement synergies. We suggest that this capacity of the motor cortex implies that it is a necessary locus for motor skill learning, which we argue is the ability to execute selected actions with increasing speed and precision.
  • Sensing with the Motor Cortex
    - Neuron 72(3):477-487 (2011)
    The primary motor cortex is a critical node in the network of brain regions responsible for voluntary motor behavior. It has been less appreciated, however, that the motor cortex exhibits sensory responses in a variety of modalities including vision and somatosensation. We review current work that emphasizes the heterogeneity in sensorimotor responses in the motor cortex and focus on its implications for cortical control of movement as well as for brain-machine interface development.
  • What Is Optimal about Motor Control?
    - Neuron 72(3):488-498 (2011)
    This article poses a controversial question: is optimal control theory useful for understanding motor behavior or is it a misdirection? This question is becoming acute as people start to conflate internal models in motor control and perception (). However, the forward models in motor control are not the generative models used in perceptual inference. This Perspective tries to highlight the differences between internal models in motor control and perception and asks whether optimal control is the right way to think about things. The issues considered here may have broader implications for optimal decision theory and Bayesian approaches to learning and behavior in general.

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