1. Neuroscience
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Energy efficient synaptic plasticity

  1. Ho Ling Li
  2. Mark CW van Rossum  Is a corresponding author
  1. University of Nottingham, United Kingdom
Research Article
  • Cited 2
  • Views 2,161
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Cite this article as: eLife 2020;9:e50804 doi: 10.7554/eLife.50804

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.

Data availability

Simulation scripts can be found at https://github.com/vanrossumlab/li_vanrossum_19.

The following previously published data sets were used

Article and author information

Author details

  1. Ho Ling Li

    School of Psychology, University of Nottingham, Nottingham, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5654-0183
  2. Mark CW van Rossum

    School of Psychology and School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
    For correspondence
    mark.vanrossum@nottingham.ac.uk
    Competing interests
    Mark CW van Rossum, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6525-6814

Funding

Leverhulme Trust (RPG-2017-404)

  • Mark CW van Rossum

Engineering and Physical Sciences Research Council (EP/R030952/1)

  • Mark CW van Rossum

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Peter Latham, University College London, United Kingdom

Publication history

  1. Received: August 3, 2019
  2. Accepted: February 10, 2020
  3. Accepted Manuscript published: February 13, 2020 (version 1)
  4. Version of Record published: March 19, 2020 (version 2)

Copyright

© 2020, Li & van Rossum

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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