Local online learning in recurrent networks with random feedback

Abstract
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Abstract

Recurrent neural networks (RNNs) enable the production and processing of time-dependent signals such as those involved in movement and working memory. Classic gradient-based algorithms for training RNNs have been available for decades, but are inconsistent with biological features of the brain, such as causality and locality. We derive an approximation to gradient-based learning that comports with these constraints by requiring synaptic weight updates to depend only on local information about pre- and postsynaptic activities, in addition to a random feedback projection of the RNN output error. In addition to providing mathematical arguments for the effectiveness of the new learning rule, we show through simulations that it can be used to train an RNN to perform a variety of tasks. Finally, to overcome the difficulty of training over very large numbers of timesteps, we propose an augmented circuit architecture that allows the RNN to concatenate short-duration patterns into longer sequences.

Data availability

Code implementing the RFLO learning algorithm for the example shown in Figure 2 has been included as a source code file accompanying this manuscript.

Article and author information

Author details

James M Murray

Zuckerman Mind, Brain, and Behavior Institute, Columbia University, New York, United States

For correspondence
jm4347@columbia.edu

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-3706-4895

Funding

National Institutes of Health (DP5 OD019897)

James M Murray

National Science Foundation (DBI-1707398)

James M Murray

Gatsby Charitable Foundation

James M Murray

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.