Energy efficient synaptic plasticity

Li, Ho Ling; van Rossum, Mark CW

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.

Citation

Li, H. L., & van Rossum, M. C. (2020). Energy efficient synaptic plasticity. eLife, 9, Article e50804. https://doi.org/10.7554/elife.50804

• Journal Article Type: Article
• Acceptance Date: Feb 10, 2020
• Online Publication Date: Feb 13, 2020
• Publication Date: Feb 13, 2020
• Deposit Date: Feb 13, 2020
• Publicly Available Date: Feb 13, 2020
• Journal: eLife
• Electronic ISSN: 2050-084X
• Publisher: eLife Sciences Publications
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Article Number: e50804
• DOI: https://doi.org/10.7554/elife.50804
• Keywords: General Biochemistry, Genetics and Molecular Biology; General Immunology and Microbiology; General Neuroscience; General Medicine
• Public URL: https://nottingham-repository.worktribe.com/output/3958706
• Publisher URL: https://elifesciences.org/articles/50804