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Sleep prevents catastrophic forgetting in spiking neural networks by forming joint synaptic weight representations

View ORCID ProfileRyan Golden, View ORCID ProfileJean Erik Delanois, Pavel Sanda, View ORCID ProfileMaxim Bazhenov
doi: https://doi.org/10.1101/688622
Ryan Golden
1Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA 92093
3Department of Medicine, University of California, San Diego, La Jolla, CA 92093
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Jean Erik Delanois
2Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA 92093
3Department of Medicine, University of California, San Diego, La Jolla, CA 92093
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Pavel Sanda
4Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic
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Maxim Bazhenov
1Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA 92093
3Department of Medicine, University of California, San Diego, La Jolla, CA 92093
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  • For correspondence: mbazhenov@ucsd.edu
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Abstract

Artificial neural networks overwrite previously learned tasks when trained sequentially, a phenomenon known as catastrophic forgetting. In contrast, the brain learns continuously, and typically learns best when new learning is interleaved with periods of sleep for memory consolidation. In this study, we used spiking network to study mechanisms behind catastrophic forgetting and the role of sleep in preventing it. The network could be trained to learn a complex foraging task but exhibited catastrophic forgetting when trained sequentially on multiple tasks. New task training moved the synaptic weight configuration away from the manifold representing old tasks leading to forgetting. Interleaving new task training with periods of off-line reactivation, mimicking biological sleep, mitigated catastrophic forgetting by pushing the synaptic weight configuration towards the intersection of the solution manifolds representing multiple tasks. The study reveals a possible strategy of synaptic weights dynamics the brain applies during sleep to prevent forgetting and optimize learning.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted June 10, 2020.
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Sleep prevents catastrophic forgetting in spiking neural networks by forming joint synaptic weight representations
Ryan Golden, Jean Erik Delanois, Pavel Sanda, Maxim Bazhenov
bioRxiv 688622; doi: https://doi.org/10.1101/688622
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Sleep prevents catastrophic forgetting in spiking neural networks by forming joint synaptic weight representations
Ryan Golden, Jean Erik Delanois, Pavel Sanda, Maxim Bazhenov
bioRxiv 688622; doi: https://doi.org/10.1101/688622

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