On Optimal Synaptic Information Storage: Shannon Meets Ramón y Cajal
Speaker: Lav Varshney ,
Experimental investigations have revealed that synapses possess interesting and, in some cases, unexpected properties. Adopting an optimization approach to biology, 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 information theoretic, based on maximizing information storage capacity of neural tissue under resource constraints. Building 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.