Literature DB >> 32516573

Neural Trajectories in the Supplementary Motor Area and Motor Cortex Exhibit Distinct Geometries, Compatible with Different Classes of Computation.

Abigail A Russo1, Ramin Khajeh2, Sean R Bittner2, Sean M Perkins3, John P Cunningham4, L F Abbott5, Mark M Churchland6.   

Abstract

The supplementary motor area (SMA) is believed to contribute to higher order aspects of motor control. We considered a key higher order role: tracking progress throughout an action. We propose that doing so requires population activity to display low "trajectory divergence": situations with different future motor outputs should be distinct, even when present motor output is identical. We examined neural activity in SMA and primary motor cortex (M1) as monkeys cycled various distances through a virtual environment. SMA exhibited multiple response features that were absent in M1. At the single-neuron level, these included ramping firing rates and cycle-specific responses. At the population level, they included a helical population-trajectory geometry with shifts in the occupied subspace as movement unfolded. These diverse features all served to reduce trajectory divergence, which was much lower in SMA versus M1. Analogous population-trajectory geometry, also with low divergence, naturally arose in networks trained to internally guide multi-cycle movement.
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  motor control; motor cortex; neural computation; neural dynamics; population coding; population geometry; recurrent neural network; supplementary motor area

Mesh:

Year:  2020        PMID: 32516573      PMCID: PMC9395139          DOI: 10.1016/j.neuron.2020.05.020

Source DB:  PubMed          Journal:  Neuron        ISSN: 0896-6273            Impact factor:   18.688


  72 in total

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Authors:  A H Lara; J P Cunningham; M M Churchland
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  19 in total

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8.  Combining Repetition Suppression and Pattern Analysis Provides New Insights into the Role of M1 and Parietal Areas in Skilled Sequential Actions.

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10.  Expectancy-based rhythmic entrainment as continuous Bayesian inference.

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