Information Transmission by Networks of Non Linear Neurons
نویسنده
چکیده
Received (to be inserted Revised by Publisher) We investigate the consequences of maximizing information transfer in a simple neural network, with bounded and invertible transfer functions. In the case of a vanishing additive output noise, and an even smaller input noise, the main result is that maximization of information (over receptive elds and transfer functions) leads to a factorial code-hence to the same solution as required by the redundancy reduction principle of Barlow.
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