Energetically efficient learning in neuronal networks

Curr Opin Neurobiol. 2023 Sep 4;83:102779. doi: 10.1016/j.conb.2023.102779. Online ahead of print.ABSTRACTHuman 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.PMID:37672980 | DOI:10.1016/j.conb.2023.102779
Source: Current Opinion in Neurobiology - Category: Neurology Authors: Source Type: research