A model for neuronal signal representation by stimulus-dependent receptive fields

Abstract

Image coding by the mammalian visual cortex has been modeled through linear combinations of receptive-field-like functions. The spatial receptive field of a visual neuron is typically assumed to be signal-independent, a view that has been challenged by recent neurophysiological findings. Motivated by these, we here propose a model for conjoint space-frequency image coding based on stimulus-dependent receptive-field-like functions. For any given frequency, the parameters of the coding functions are obtained from the Fourier transform of the stimulus. The representation is initially presented in terms of Gabor functions, but can be extended to more general forms, and we find that the resulting coding functions show properties that are consistent with those of the receptive fields of simple cortical cells of the macaque. © 2009 Springer Berlin Heidelberg.