What can we learn from synaptic weight distributions?
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Updated: Jul 10, 2026

3D Modeling of Dendritic Spines with Synaptic Plasticity
Published on: May 18, 2020
Boris Barbour1, Nicolas Brunel, Vincent Hakim
1Laboratoire de Neurobiologie (CNRS UMR 8544), Ecole Normale Supérieure, 46 rue d'Ulm, 75005 Paris, France. barbour@ens.fr
Abstract:
Much research effort into synaptic plasticity has been motivated by the idea that modifications of synaptic weights (or strengths or efficacies) underlie learning and memory. Here, we examine the possibility of exploiting the statistics of experimentally measured synaptic weights to deduce information about the learning process. Analysing distributions of synaptic weights requires a theoretical framework to interpret the experimental measurements, but the results can be unexpectedly powerful, yielding strong constraints on possible learning theories as well as information that is difficult to obtain by other means, such as the information storage capacity of a cell. We review the available experimental and theoretical techniques as well as important open issues.
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