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Energy efficient synaptic plasticity

Many aspects of the brain’s design can be understood as the result of evolutionary drive toward 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 pl...

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Detalles Bibliográficos
Autores principales: Li, Ho Ling, van Rossum, Mark CW
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7082127/
https://www.ncbi.nlm.nih.gov/pubmed/32053106
http://dx.doi.org/10.7554/eLife.50804
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author Li, Ho Ling
van Rossum, Mark CW
author_facet Li, Ho Ling
van Rossum, Mark CW
author_sort Li, Ho Ling
collection PubMed
description Many aspects of the brain’s design can be understood as the result of evolutionary drive toward 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.
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spelling pubmed-70821272020-03-23 Energy efficient synaptic plasticity Li, Ho Ling van Rossum, Mark CW eLife Neuroscience Many aspects of the brain’s design can be understood as the result of evolutionary drive toward 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. eLife Sciences Publications, Ltd 2020-02-13 /pmc/articles/PMC7082127/ /pubmed/32053106 http://dx.doi.org/10.7554/eLife.50804 Text en © 2020, Li and van Rossum http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Neuroscience
Li, Ho Ling
van Rossum, Mark CW
Energy efficient synaptic plasticity
title Energy efficient synaptic plasticity
title_full Energy efficient synaptic plasticity
title_fullStr Energy efficient synaptic plasticity
title_full_unstemmed Energy efficient synaptic plasticity
title_short Energy efficient synaptic plasticity
title_sort energy efficient synaptic plasticity
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7082127/
https://www.ncbi.nlm.nih.gov/pubmed/32053106
http://dx.doi.org/10.7554/eLife.50804
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