TY - JOUR
T1 - Optimal Information Storage in Noisy Synapses under Resource Constraints
AU - Varshney, Lav R.
AU - Sjöström, Per Jesper
AU - Chklovskii, Dmitri B B.
N1 - We are grateful to M. DeWeese, A. Koulakov, C. Machens, R. Malinow, and S.K. Mitter for commenting on the early versions of the manuscript and to A. Roth, Y. Mishchenko, M. H\u00E4usser, L. Srinivasan, and S.B. Nelson for discussions. We also thank the anonymous reviewers and the editor for helping clarify our presentation. This work was supported by the NIH Grant MH69838, the Klingenstein Foundation Award, an NSF Graduate Research Fellowship, the NSF Grant CCR-0325774, the Wellcome Trust, and an EU Marie Curie grant.
PY - 2006/11/9
Y1 - 2006/11/9
N2 - 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.
AB - 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.
KW - SYSNEURO
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U2 - 10.1016/j.neuron.2006.10.017
DO - 10.1016/j.neuron.2006.10.017
M3 - Article
C2 - 17088208
AN - SCOPUS:33750520352
SN - 0896-6273
VL - 52
SP - 409
EP - 423
JO - Neuron
JF - Neuron
IS - 3
ER -