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      Independent generation of sequence elements by motor cortex

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      1 , 2 , 1 , 2 , 3 , 4 , *
      Nature neuroscience

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          Abstract

          Rapid execution of motor sequences is believed to depend upon fusing movement elements into cohesive units that are executed holistically. We sought to determine the contribution of primary motor and dorsal premotor cortex to this ability. Monkeys performed highly practiced two-reach sequences, interleaved with matched reaches performed alone or separated by a delay. We partitioned neural population activity into components pertaining to preparation, initiation, and execution. The hypothesis that movement elements fuse makes specific predictions regarding all three forms of activity. We observed none of these predicted effects. Rapid two-reach sequences involved the same set of neural events as individual reaches but with preparation for the second reach occurring as the first was in flight. Thus, at the level of dorsal premotor and primary motor cortex, skillfully executing a rapid sequence depends not on fusing elements, but on the ability to perform two key processes at the same time.

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          Most cited references51

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          Neural population dynamics during reaching

          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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            A neural network that finds a naturalistic solution for the production of muscle activity.

            It remains an open question how neural responses in motor cortex relate to movement. We explored the hypothesis that motor cortex reflects dynamics appropriate for generating temporally patterned outgoing commands. To formalize this hypothesis, we trained recurrent neural networks to reproduce the muscle activity of reaching monkeys. Models had to infer dynamics that could transform simple inputs into temporally and spatially complex patterns of muscle activity. Analysis of trained models revealed that the natural dynamical solution was a low-dimensional oscillator that generated the necessary multiphasic commands. This solution closely resembled, at both the single-neuron and population levels, what was observed in neural recordings from the same monkeys. Notably, data and simulations agreed only when models were optimized to find simple solutions. An appealing interpretation is that the empirically observed dynamics of motor cortex may reflect a simple solution to the problem of generating temporally patterned descending commands.
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              Is Open Access

              Demixed principal component analysis of neural population data

              Neurons in higher cortical areas, such as the prefrontal cortex, are often tuned to a variety of sensory and motor variables, and are therefore said to display mixed selectivity. This complexity of single neuron responses can obscure what information these areas represent and how it is represented. Here we demonstrate the advantages of a new dimensionality reduction technique, demixed principal component analysis (dPCA), that decomposes population activity into a few components. In addition to systematically capturing the majority of the variance of the data, dPCA also exposes the dependence of the neural representation on task parameters such as stimuli, decisions, or rewards. To illustrate our method we reanalyze population data from four datasets comprising different species, different cortical areas and different experimental tasks. In each case, dPCA provides a concise way of visualizing the data that summarizes the task-dependent features of the population response in a single figure. DOI: http://dx.doi.org/10.7554/eLife.10989.001
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                Author and article information

                Journal
                9809671
                21092
                Nat Neurosci
                Nat Neurosci
                Nature neuroscience
                1097-6256
                1546-1726
                24 January 2021
                22 February 2021
                March 2021
                22 August 2021
                : 24
                : 3
                : 412-424
                Affiliations
                [1 ]Department of Neuroscience, Columbia University Medical Center, New York, New York, USA.
                [2 ]Zuckerman Institute, Columbia University, New York, New York, USA.
                [3 ]Kavli Institute for Brain Science, Columbia University Medical Center, New York, New York, USA.
                [4 ]Grossman Center for the Statistics of Mind, Columbia University Medical Center, New York, New
                Author notes

                Author contributions

                A.J.Z and M.M.C conceived the experiments, designed the data analyses, and wrote and edited the manuscript. A.J.Z. collected the data and performed the analyses.

                [* ]Correspondence: mc3502@ 123456columbia.edu
                Article
                NIHMS1662713
                10.1038/s41593-021-00798-5
                7933118
                33619403
                6da17aed-9345-48b1-8d5d-4d1f103f33c0

                Users may view, print, copy, and download 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

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