What can we learn from synaptic weight distributions?
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.
Barbour
Boris
B
Laboratoire de Neurobiologie (CNRS UMR 8544), Ecole Normale Supérieure, 46 rue d'Ulm, 75005 Paris, France. barbour@ens.fr
Brunel
Nicolas
N
Hakim
Vincent
V
Nadal
Jean-Pierre
JP
D000818:Animals; D001921:Brain / Q000166:cytology / Q000502:physiology*; D006801:Humans; D007858:Learning / Q000502:physiology*; D008568:Memory / Q000502:physiology; D009473:Neuronal Plasticity / Q000502:physiology*; D009474:Neurons / Q000145:classification / Q000166:cytology / Q000502:physiology*; D013569:Synapses / Q000145:classification / Q000502:physiology*