ArticleeLife2022
Natural-gradient learning for spiking neurons.
Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Spike-based alignment learning solves the weight transport problem.Nature communications · 2026Article
- Article
- The coming decade of digital brain research: A vision for neuroscience at the intersection of technology and computing.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Natural gradient enables fast sampling in spiking neural networks.Advances in neural information processing systems · 2022Article
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Authors and funding
3 authors.
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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.
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