Neural network models of the tactile system develop first-order units with spatially complex receptive fields

Authors

Charlie W. Zhao, The University of Western Ontario
Mark J. Daley, The University of Western OntarioFollow
J. Andrew Pruszynski, The University of Western Ontario

Document Type

Article

Publication Date

6-1-2018

Journal

PLoS ONE

Volume

13

Issue

6

URL with Digital Object Identifier

10.1371/journal.pone.0199196

Abstract

First-order tactile neurons have spatially complex receptive fields. Here we use machine-learning tools to show that such complexity arises for a wide range of training sets and network architectures. Moreover, we demonstrate that this complexity benefits network performance, especially on more difficult tasks and in the presence of noise. Our work suggests that spatially complex receptive fields are normatively good given the biological constraints of the tactile periphery.