OPTIMAL INFORMATION STORAGE
IN NOISY SYNAPSES |
Lav R. Varshney1,2 , Per Jesper Sjöström3
and Dmitri B. Chklovskii2 1 Department of Electrical
Engineering and Computer Science, MIT, Cambridge, MA 2 Cold
Spring Harbor Laboratory, Cold Spring Harbor, NY 3 Wolfson Institute for Biomedical
Research and Department of Physiology, University College |
|
Experimental investigations have revealed that synapses
possess interesting and, in some cases, unexpected properties. We propose a
theoretical framework that accounts for four of these properties: typical
central synapses are noisy; the distribution of synaptic weights among
central synapses is wide; synaptic connectivity between neurons is sparse;
and synaptic weights may vary in discrete steps. Our approach is based on maximizing information storage
capacity of neural tissue under resource constraint. 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. |