Engineering recurrent neural networks from task-relevant manifolds and dynamics
Name
pcbi.1008128.pdf
Description
Published version
Size
2.8 MB
Format
Adobe PDF
Checksum (MD5)
c1fd46f2ac4ca4f987e48372bb5c3e7e
Author(s)
Pollock, Eli
Jazayeri, Mehrdad
Date Issued
2020
Journal
PLoS Computational Biology
Publisher
Public Library of Science (PLoS)
Version
Final published version
Abstract
Copyright: © 2020 Pollock, Jazayeri. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Many cognitive processes involve transformations of distributed representations in neural populations, creating a need for population-level models. Recurrent neural network models fulfill this need, but there are many open questions about how their connectivity gives rise to dynamics that solve a task. Here, we present a method for finding the connectivity of networks for which the dynamics are specified to solve a task in an interpretable way. We apply our method to a working memory task by synthesizing a network that implements a drift-diffusion process over a ring-shaped manifold. We also use our method to demonstrate how inputs can be used to control network dynamics for cognitive flexibility and explore the relationship between representation geometry and network capacity. Our work fits within the broader context of understanding neural computations as dynamics over relatively low-dimensional manifolds formed by correlated patterns of neurons.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
McGovern Institute for Brain Research at MIT
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1371/JOURNAL.PCBI.1008128
