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      Neural population dynamics during reaching

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          Abstract

          Most theories of motor cortex have assumed that neural activity represents movement parameters. This view derives from an analogous approach to primary visual cortex, where neural activity represents patterns of light. Yet it is unclear how well that analogy holds. Single-neuron responses in motor cortex appear strikingly complex, and there is marked disagreement regarding which movement parameters are represented. A better analogy might be with other motor systems, where a common principle is rhythmic neural activity. We found that motor cortex responses during reaching contain a brief but strong oscillatory component, something quite unexpected for a non-periodic behavior. Oscillation amplitude and phase followed naturally from the preparatory state, suggesting a mechanistic role for preparatory neural activity. These results demonstrate unexpected yet surprisingly simple structure in the population response. That underlying structure explains many of the confusing features of individual-neuron responses.

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          Most cited references36

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          Generating coherent patterns of activity from chaotic neural networks.

          Neural circuits display complex activity patterns both spontaneously and when responding to a stimulus or generating a motor output. How are these two forms of activity related? We develop a procedure called FORCE learning for modifying synaptic strengths either external to or within a model neural network to change chaotic spontaneous activity into a wide variety of desired activity patterns. FORCE learning works even though the networks we train are spontaneously chaotic and we leave feedback loops intact and unclamped during learning. Using this approach, we construct networks that produce a wide variety of complex output patterns, input-output transformations that require memory, multiple outputs that can be switched by control inputs, and motor patterns matching human motion capture data. Our results reproduce data on premovement activity in motor and premotor cortex, and suggest that synaptic plasticity may be a more rapid and powerful modulator of network activity than generally appreciated.
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            Neuronal population coding of movement direction.

            Although individual neurons in the arm area of the primate motor cortex are only broadly tuned to a particular direction in three-dimensional space, the animal can very precisely control the movement of its arm. The direction of movement was found to be uniquely predicted by the action of a population of motor cortical neurons. When individual cells were represented as vectors that make weighted contributions along the axis of their preferred direction (according to changes in their activity during the movement under consideration) the resulting vector sum of all cell vectors (population vector) was in a direction congruent with the direction of movement. This population vector can be monitored during various tasks, and similar measures in other neuronal populations could be of heuristic value where there is a neural representation of variables with vectorial attributes.
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              Optimal feedback control and the neural basis of volitional motor control.

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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                20 April 2012
                5 July 2012
                05 January 2013
                : 487
                : 7405
                : 51-56
                Affiliations
                [1 ]Dept. of Neuroscience, Kavli Institute for Brain Science, David Mahoney Center, Columbia University Medical Center, New York NY, 10032 USA
                [2 ]Dept. of Electrical Engineering, Stanford University, Stanford CA, 94305, USA
                [3 ]Neurosciences Program, Stanford University, Stanford CA, 94305, USA
                [4 ]Dept. of Biomedical Engineering, Washington University in St. Louis, St. Louis MO, 63130
                [5 ]Dept. of Engineering, University of Cambridge, Cambridge, CB2 1PZ UK
                [6 ]Dept. of Bioengineering, Stanford University, Stanford CA, 94705, USA
                [7 ]Stanford University School of Medicine, Stanford CA, 94305, USA
                [8 ]Dept. of Neurosurgery, Palo Alto Medical Foundation, Palo Alto CA, 94301, USA
                [9 ]Dept. of Neurobiology, Stanford University School of Medicine, Stanford CA, 94305, USA
                Author notes
                Correspondence should be addressed to: Mark Churchland mc3502@ 123456columbia.edu
                Article
                NIHMS368883
                10.1038/nature11129
                3393826
                22722855
                27f50584-70a2-4cf8-9548-796c063655f4

                Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                Funding
                Funded by: National Institute of Neurological Disorders and Stroke : NINDS
                Award ID: R01 NS054283-01 || NS
                Funded by: Office of the Director : NIH
                Award ID: DP1 OD006409-01 || OD
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