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Energy efficient synaptic plasticity

Li, Ho Ling; van Rossum, Mark CW

Authors

Ho Ling Li

Mark CW van Rossum



Abstract

Many aspects of the brain's design can be understood as the result of evolutionary drive towards metabolic efficiency. In addition to the energetic costs of neural computation and transmission, experimental evidence indicates that synaptic plasticity is metabolically demanding as well. As synaptic plasticity is crucial for learning, we examine how these metabolic costs enter in learning. We find that when synaptic plasticity rules are naively implemented, training neural networks requires extremely large amounts of energy when storing many patterns. We propose that this is avoided by precisely balancing labile forms of synaptic plasticity with more stable forms. This algorithm, termed synaptic caching, boosts energy efficiency manifold and can be used with any plasticity rule, including back-propagation. Our results yield a novel interpretation of the multiple forms of neural synaptic plasticity observed experimentally, including synaptic tagging and capture phenomena. Furthermore our results are relevant for energy efficient neuromorphic designs.

Journal Article Type Article
Publication Date Feb 13, 2020
Journal eLife
Publisher eLife Sciences Publications
Peer Reviewed Peer Reviewed
Volume 9
Article Number e50804
APA6 Citation Li, H. L., & van Rossum, M. C. (2020). Energy efficient synaptic plasticity. eLife, 9, https://doi.org/10.7554/elife.50804
DOI https://doi.org/10.7554/elife.50804
Keywords General Biochemistry, Genetics and Molecular Biology; General Immunology and Microbiology; General Neuroscience; General Medicine
Publisher URL https://elifesciences.org/articles/50804

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