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Elife. 2018 Sep 20;7. pii: e37124. doi: 10.7554/eLife.37124.

Learning recurrent dynamics in spiking networks.

Author information

1
Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, United States.

Abstract

Spiking activity of neurons engaged in learning and performing a task show complex spatiotemporal dynamics. While the output of recurrent network models can learn to perform various tasks, the possible range of recurrent dynamics that emerge after learning remains unknown. Here we show that modifying the recurrent connectivity with a recursive least squares algorithm provides sufficient flexibility for synaptic and spiking rate dynamics of spiking networks to produce a wide range of spatiotemporal activity. We apply the training method to learn arbitrary firing patterns, stabilize irregular spiking activity in a network of excitatory and inhibitory neurons respecting Dale's law, and reproduce the heterogeneous spiking rate patterns of cortical neurons engaged in motor planning and movement. We identify sufficient conditions for successful learning, characterize two types of learning errors, and assess the network capacity. Our findings show that synaptically-coupled recurrent spiking networks possess a vast computational capability that can support the diverse activity patterns in the brain.

KEYWORDS:

computational biology; learning; neuroscience; none; recurrent dynamics; spiking network; systems biology; universal dynamics

PMID:
30234488
PMCID:
PMC6195349
DOI:
10.7554/eLife.37124
[Indexed for MEDLINE]
Free PMC Article

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