Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive tasks.

Content Provider	Europe PMC
Author	Miconi, Thomas
Editor	Frank, Michael J
Copyright Year	2017
Abstract	Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior.DOI: http://dx.doi.org/10.7554/eLife.20899.001
Journal	eLife
Volume Number	6
PubMed Central reference number	PMC5398889
PubMed reference number	28230528
e-ISSN	2050084X
DOI	10.7554/elife.20899
Language	English
Publisher	eLife Sciences Publications, Ltd
Publisher Date	2017-02-23
Access Restriction	Open
Rights License	This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited. © 2017, Miconi et al
Subject Keyword	computational neuroscience learning modeling recurrent neural networks cognition
Content Type	Text
Resource Type	Article
Subject	Immunology and Microbiology Neuroscience Medicine Biochemistry, Genetics and Molecular Biology