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Roman Pogodin

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Publications

Brain-like learning with exponentiated gradients
Jonathan Cornford
Roman Pogodin
Arna Ghosh
Kaiwen Sheng
Brendan A. Bicknell
Olivier Codol
Beverley A. Clark
Guillaume Lajoie
Blake Richards
2024-10-26
bioRxiv (prépublication)
doi.org
Synaptic Weight Distributions Depend on the Geometry of Plasticity
Roman Pogodin
Jonathan Cornford
Arna Ghosh
Gauthier Gidel
Guillaume Lajoie
Blake Richards
A growing literature in computational neuroscience leverages gradient descent and learning algorithms that approximate it to study synaptic … (voir plus)plasticity in the brain. However, the vast majority of this work ignores a critical underlying assumption: the choice of distance for synaptic changes - i.e. the geometry of synaptic plasticity. Gradient descent assumes that the distance is Euclidean, but many other distances are possible, and there is no reason that biology necessarily uses Euclidean geometry. Here, using the theoretical tools provided by mirror descent, we show that the distribution of synaptic weights will depend on the geometry of synaptic plasticity. We use these results to show that experimentally-observed log-normal weight distributions found in several brain areas are not consistent with standard gradient descent (i.e. a Euclidean geometry), but rather with non-Euclidean distances. Finally, we show that it should be possible to experimentally test for different synaptic geometries by comparing synaptic weight distributions before and after learning. Overall, our work shows that the current paradigm in theoretical work on synaptic plasticity that assumes Euclidean synaptic geometry may be misguided and that it should be possible to experimentally determine the true geometry of synaptic plasticity in the brain.
2024-01-16
ICLR.cc/2024/Conference (spotlight)
doi.org
openreview.net