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 synapt...

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Bibliographic Details
Published in:eLife 2020-02, Vol.9
Main Authors: Li, Ho Ling, van Rossum, Mark Cw
Format: Article
Language:English
Subjects:
Algorithms
computational models
Costs
Energy efficiency
Experiments
Humans
Machine Learning
Metabolism
Neural networks
Neural Networks, Computer
Neuronal Plasticity
Neuroscience
Plasticity (neural)
synaptic consolidation
Synaptic plasticity
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Summary: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.
ISSN:2050-084X
2050-084X
DOI:10.7554/eLife.50804