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 London 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.