Optimal Information Storage in Noisy Synapses under Resource Constraints

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 wi...

Full description

Saved in:
Bibliographic Details
Published in:Neuron (Cambridge, Mass.) Mass.), 2006-11, Vol.52 (3), p.409-423
Main Authors: Varshney, Lav R., Sjöström, Per Jesper, Chklovskii, Dmitri B.
Format: Article
Language:English
Subjects:
Animals
Entropy
Excitatory Postsynaptic Potentials - physiology
Information storage
Information Storage and Retrieval
Information theory
Memory - physiology
Models, Neurological
Neurons
Noise
Random variables
Standard deviation
Synapses - physiology
Synaptic Transmission - physiology
SYSNEURO
Theory
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0896-6273
1097-4199
DOI:10.1016/j.neuron.2006.10.017