An alternative approach to infomax and independent component analysis

Abstract

Infomax means maximization of information flow in a neural system. A nonlinear version of infomax has been shown to be connected to independent component analysis and the receptive fields of neurons in the visual cortex. Here we show a problem of nonrobustness of nonlinear infomax: it is very sensitive to the choice the nonlinear neuronal transfer function. We consider an alternative approach in which the system is linear, but the noise level depends on the mean of the signal, as in a Poisson neuron model. This gives similar predictions as the nonlinear infomax, but seems to be more robust.