Abstract
Reverse-correlation is the most widely used method for mapping receptive fields of early visual neurons. Wiener kernels of the neurons are calculated by cross-correlating the neuronal responses with a Gaussian white noise stimulus. However, Gaussian white noise is an inefficient stimulus for driving higher-level visual neurons. We show that if the stimulus is synthesized by a linear generative model such that its statistics approximate that of natural images, a simple solution for the kernels can be derived.
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