Optimal Information Storage in Noisy Synapses under Resource Constraints

Lav R. Varshney, Per Jesper Sjöström, Dmitri B B. Chklovskii

Research output: Contribution to journalArticlepeer-review

Abstract

Experimental investigations have revealed that synapses possess interesting and, in some cases, unexpected properties. We propose a theoretical framework that accounts for three of these properties: typical central synapses are noisy, the distribution of synaptic weights among central synapses is wide, and synaptic connectivity between neurons is sparse. We also comment on the possibility that synaptic weights may vary in discrete steps. Our approach is based on maximizing information storage capacity of neural tissue under resource constraints. Based on previous experimental and theoretical work, we use volume as a limited resource and utilize the empirical relationship between volume and synaptic weight. Solutions of our constrained optimization problems are not only consistent with existing experimental measurements but also make nontrivial predictions.

Original languageEnglish (US)
Pages (from-to)409-423
Number of pages15
JournalNeuron
Volume52
Issue number3
DOIs
StatePublished - Nov 9 2006

Keywords

  • SYSNEURO

ASJC Scopus subject areas

  • Neuroscience(all)

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