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Indistinguishable synapses lead to sparse networks. (English) Zbl 1472.92041

Summary: Neurons integrate information from many neighbors when they process information. Inputs to a given neuron are thus indistinguishable from one another. Under the assumption that neurons maximize their information storage, indistinguishability is shown to place a strong constraint on the distribution of strengths between neurons. The distribution of individual synapse strengths is found to follow a modified Boltzmann distribution with strength proportional to \(\exp (-\beta w)\). The model is shown to be consistent with experimental data from Caenorhabditis elegans connectivity and in vivo synaptic strength measurements. The \(1/w\) dependence helps account for the observation of many zero or weak connections between neurons or sparsity of the neural network.

MSC:

92B20 Neural networks for/in biological studies, artificial life and related topics
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