Energetically efficient learning in neuronal networks

Pache, Aaron; van Rossum, Mark C.W.

Abstract

Human and animal experiments have shown that acquiring and storing information can require substantial amounts of metabolic energy. However, computational models of neural plasticity only seldom take this cost into account, and might thereby miss an important constraint on biological learning. This review explores various ways to reduce energy requirements for learning in neural networks. By comparing the resulting learning rules to cognitive and neurophysiological observations, we discuss how energy efficiency might have shaped biological learning.

Citation

Pache, A., & van Rossum, M. C. (2023). Energetically efficient learning in neuronal networks. Current Opinion in Neurobiology, 83, Article 102779. https://doi.org/10.1016/j.conb.2023.102779

• Journal Article Type: Article
• Acceptance Date: Aug 9, 2023
• Online Publication Date: Sep 4, 2023
• Publication Date: 2023-12
• Deposit Date: Sep 9, 2023
• Publicly Available Date: Sep 11, 2023
• Journal: Current Opinion in Neurobiology
• Print ISSN: 0959-4388
• Electronic ISSN: 1873-6882
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 83
• Article Number: 102779
• DOI: https://doi.org/10.1016/j.conb.2023.102779
• Public URL: https://nottingham-repository.worktribe.com/output/25082120
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S0959438823001046?via%3Dihub