UCSD Cogsci Tech Report Announcement

[Javier R. Movellan]

UCSD Cognitive Science Tech Report 

Author: Sohie Lee

Communicated by: David Zipser

Title: The Representation, Storage and 
       Retrieval of Reaching Movement Information 
       in Motor Cortex.

Electronic copies: http://cogsci.ucsd.edu and click on "Tech Reports and Software"

Physical copies:  Available for $7.00 within the US, $10.00 outside the US.

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TR Request
Javier R. Movellan
Department of Cognitive Science
University of California San Diego
La Jolla, Ca 92093-0515



                                 ABSTRACT 

This report describes the use of analytical techniques and recurrent neural
networks to investigate the representation and storage of reaching movement
information. A key feature of reaching movement representation revealed by
single cell recording is the firing of individual neurons to preferred
movement directions.

The preferred directions of motor cortical neurons change with starting hand
position during reaching. I confirm that the precise nature of tuning
parameters' spatial modulation is dependent upon afferent format.  I also show
that nonlinear coordinate systems produce the spatially dependent tuning
parameters of the general form required by experimental observation. 
A model that investigates the dynamics of movement representation in motor
cortex is described.  A fully recurrent neural network was trained to
continually output the direction and magnitude of movements required to reach
randomly changing targets.  Model neurons developed preferred directions and
other properties similar to real motor cortical neurons. The key finding is
that when the target for a reaching movement changes location, the ensemble
representation of the movement changes nearly monotonically, while the
individual neurons comprising the representation exhibit strong, nonmonotonic
transients. These transients serve as internal recurrent signals that force
the ensemble representation to change more rapidly than if it were limited by
the time constants of individual neurons. These transients can be tested for
experimentally.

A second model investigates how recurrent networks might implement the
storage, retrieval and matching functions observed when monkeys are trained to
perform delayed match-to-sample reaching tasks with distractors.  A fully 
recurrent network was trained to perform the task.  The model learns a storage
mechanism that relies on fixed point attractors.  A minimal-sized network is
comprised of units that correspond to the various task components, whereas 
larger networks exhibit more distributed solutions and have neuron properties 
that more closely resemble single cell behavior in the brain.