Literature DB >> 11709483

A model of movement coordinates in the motor cortex: posture-dependent changes in the gain and direction of single cell tuning curves.

R Ajemian1, D Bullock, S Grossberg.   

Abstract

This article outlines a methodology for investigating the coordinate systems by which movement variables are encoded in the firing rates of individual motor cortical neurons. Recent neurophysiological experiments have probed the issue of underlying coordinates by examining how cellular preferred directions (as determined by the center-out task) change with posture. Several key experimental findings have resulted that constrain hypotheses about how motor cortical cells encode movement information. But while the significance of shifts in preferred direction is well known and widely accepted, posture-dependent changes in the depth of modulation of a cell's tuning curve--that is, gain changes--have not been similarly identified as a means of coordinate inference. This article develops a vector field framework in which the preferred direction and the gain of a cell's tuning curve are viewed as dual components of a unitary response vector. The formalism can be used to compute how each aspect of cell response covaries with posture as a function of the coordinate system in which a given cell is hypothesized to encode its movement information. Such an integrated approach leads to a model of motor cortical cell activity that codifies the following four observations: (i) cell activity correlates with hand movement direction; (ii) cell activity correlates with hand movement speed; (iii) preferred directions vary with posture; and (iv) the modulation depth of tuning curves varies with posture. Finally, the model suggests general methods for testing coordinate hypotheses at the single-cell level and simulates an example protocol for three possible coordinate systems: Cartesian spatial, shoulder-centered, and joint angle.

Mesh:

Year:  2001        PMID: 11709483     DOI: 10.1093/cercor/11.12.1124

Source DB:  PubMed          Journal:  Cereb Cortex        ISSN: 1047-3211            Impact factor:   5.357


  9 in total

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Journal:  Hum Mov Sci       Date:  2004-11       Impact factor: 2.161

2.  Optimal sensorimotor integration in recurrent cortical networks: a neural implementation of Kalman filters.

Authors:  Sophie Denève; Jean-René Duhamel; Alexandre Pouget
Journal:  J Neurosci       Date:  2007-05-23       Impact factor: 6.167

3.  Motor adaptation and generalization of reaching movements using motor primitives based on spatial coordinates.

Authors:  Hirokazu Tanaka; Terrence J Sejnowski
Journal:  J Neurophysiol       Date:  2014-11-26       Impact factor: 2.714

4.  Computing reaching dynamics in motor cortex with Cartesian spatial coordinates.

Authors:  Hirokazu Tanaka; Terrence J Sejnowski
Journal:  J Neurophysiol       Date:  2012-10-31       Impact factor: 2.714

5.  Movement representation in the primary motor cortex and its contribution to generalizable EMG predictions.

Authors:  Emily R Oby; Christian Ethier; Lee E Miller
Journal:  J Neurophysiol       Date:  2012-11-14       Impact factor: 2.714

6.  Primary motor cortical discharge during force field adaptation reflects muscle-like dynamics.

Authors:  Anil Cherian; Hugo L Fernandes; Lee E Miller
Journal:  J Neurophysiol       Date:  2013-05-08       Impact factor: 2.714

7.  Feature interactions enable decoding of sensorimotor transformations for goal-directed movement.

Authors:  Deborah A Barany; Valeria Della-Maggiore; Shivakumar Viswanathan; Matthew Cieslak; Scott T Grafton
Journal:  J Neurosci       Date:  2014-05-14       Impact factor: 6.167

Review 8.  Cerebellar encoding of limb position.

Authors:  Antonino Casabona; Maria Stella Valle; Gianfranco Bosco; Vincenzo Perciavalle
Journal:  Cerebellum       Date:  2004       Impact factor: 3.648

Review 9.  From Parametric Representation to Dynamical System: Shifting Views of the Motor Cortex in Motor Control.

Authors:  Tianwei Wang; Yun Chen; He Cui
Journal:  Neurosci Bull       Date:  2022-03-17       Impact factor: 5.271

  9 in total

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