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0899-7667
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1530-888X
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2.21

Neural Computation

April 2017, Vol. 29, No. 4, Pages 888-896
(doi: 10.1162/NECO_a_00943)
© 2017 Massachusetts Institute of Technology
Information Maximization Explains the Sparseness of Presynaptic Neural Response
Article PDF (259.05 KB)
Abstract

In a sensory neural network, where a population of presynaptic neurons sends information to a downstream neuron, maximizing information transmission depends on utilizing the full operating range of the output of the postsynaptic neuron. Because the convergence of presynaptic inputs naturally biases higher outputs, a sparse input distribution would counter such bias and optimize information transmission.