A multi-stage recurrent neural network better describes decision-related activity in dorsal premotor cortex
Files
Accepted manuscript
Date
2019
Authors
Kleinman, Michael
Chandrasekaran, Chandramouli
Kao, Jonathan
Version
OA Version
Accepted manuscript
Citation
Michael Kleinman, Chandramouli Chandrasekaran, Jonathan Kao. 2019. "A multi-stage recurrent neural network better describes decision-related activity in dorsal premotor cortex." 2019 Conference on Cognitive Computational Neuroscience. 2019 Conference on Cognitive Computational Neuroscience. 2019-09-13 - 2019-09-16. https://doi.org/10.32470/ccn.2019.1123-0
Abstract
We studied how a network of recurrently connected
artificial units solve a visual perceptual decision-making
task. The goal of this task is to discriminate the dominant
color of a central static checkerboard and report the
decision with an arm movement. This task has been used
to study neural activity in the dorsal premotor (PMd)
cortex. When a single recurrent neural network (RNN)
was trained to perform the task, the activity of artificial
units in the RNN differed from neural recordings in PMd,
suggesting that inputs to PMd differed from inputs to the
RNN. We expanded our architecture and examined how
a multi-stage RNN performed the task. In the multi-stage
RNN, the last stage exhibited similarities with PMd by
representing direction information but not color
information. We then investigated how the
representation of color and direction information evolve
across RNN stages. Together, our results are a
demonstration of the importance of incorporating
architectural constraints into RNN models. These
constraints can improve the ability of RNNs to model
neural activity in association areas.
Description
License
"This work is licensed under the Creative Commons Attribution 3.0 Unported License." dc.rights.uri: http://creativecommons.org/licenses/by/3.0