Optimal Information Storage in Noisy Synapses

Dmitri 'Mitya' Chklovskii

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 non-trivial predictions.