Natural-gradient learning for spiking neurons.
Publisher DOI
PubMed ID
35467527
Abstract
In many normative theories of synaptic plasticity, weight updates implicitly depend on the chosen parametrization of the weights. This problem relates, for example, to neuronal morphology: synapses which are functionally equivalent in terms of their impact on somatic firing can differ substantially in spine size due to their different positions along the dendritic tree. Classical theories based on Euclidean-gradient descent can easily lead to inconsistencies due to such parametrization dependence. The issues are solved in the framework of Riemannian geometry, in which we propose that plasticity instead follows natural-gradient descent. Under this hypothesis, we derive a synaptic learning rule for spiking neurons that couples functional efficiency with the explanation of several well-documented biological phenomena such as dendritic democracy, multiplicative scaling, and heterosynaptic plasticity. We therefore suggest that in its search for functional synaptic plasticity, evolution might have come up with its own version of natural-gradient descent.
Date Issued
2022-04-25
Publication Type
Article
Subject(s)
Subjects
computational biology dendritic learning efficient learning homeostasis natural-gradient descent neuroscience none parametrization invariance synaptic plasticity systems biology
Language(s)
en
Additional Credits
Journal
eLife
Publisher
eLife Sciences Publications
ISSN
2050-084X
Access(Rights)
open.access