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  • Title: Nonlinear feedforward networks with stochastic outputs: infomax implies redundancy reduction.
    Author: Nadal JP, Brunel N, Parga N.
    Journal: Network; 1998 May; 9(2):207-17. PubMed ID: 9861986.
    Abstract:
    We prove that maximization of mutual information between the output and the input of a feedforward neural network leads to full redundancy reduction under the following sufficient conditions: (i) the input signal is a (possibly nonlinear) invertible mixture of independent components; (ii) there is no input noise; (iii) the activity of each output neuron is a (possibly) stochastic variable with a probability distribution depending on the stimulus through a deterministic function of the inputs (where both the probability distributions and the functions can be different from neuron to neuron); (iv) optimization of the mutual information is performed over all these deterministic functions. This result extends that obtained by Nadal and Parga (1994) who considered the case of deterministic outputs.
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