Optimal information storage and the distribution of synaptic weights: perceptron versus Purkinje cell.

Published

Journal Article

It is widely believed that synaptic modifications underlie learning and memory. However, few studies have examined what can be deduced about the learning process from the distribution of synaptic weights. We analyze the perceptron, a prototypical feedforward neural network, and obtain the optimal synaptic weight distribution for a perceptron with excitatory synapses. It contains more than 50% silent synapses, and this fraction increases with storage reliability: silent synapses are therefore a necessary byproduct of optimizing learning and reliability. Exploiting the classical analogy between the perceptron and the cerebellar Purkinje cell, we fitted the optimal weight distribution to that measured for granule cell-Purkinje cell synapses. The two distributions agreed well, suggesting that the Purkinje cell can learn up to 5 kilobytes of information, in the form of 40,000 input-output associations.

Full Text

Duke Authors

Cited Authors

  • Brunel, N; Hakim, V; Isope, P; Nadal, J-P; Barbour, B

Published Date

  • September 2, 2004

Published In

Volume / Issue

  • 43 / 5

Start / End Page

  • 745 - 757

PubMed ID

  • 15339654

Pubmed Central ID

  • 15339654

International Standard Serial Number (ISSN)

  • 0896-6273

Digital Object Identifier (DOI)

  • 10.1016/j.neuron.2004.08.023

Language

  • eng

Conference Location

  • United States