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Neurons in primary visual cortex (V1) are commonly classified as simple
or complex based upon their sensitivity to the sign of stimulus contrast.
The responses of both cell types can be described by a general model
in which the outputs of a set of linear filters are nonlinearly combined.
We estimated the model for a population of V1 neurons by analyzing the
mean and covariance of the spatiotemporal distribution of random bar
stimuli that were associated with spikes. This analysis reveals an unsuspected
richness of neuronal computation within V1. Specifically, simple and
complex cell responses are best described using more linear filters
than the one or two found in standard models. Many filters revealed
by the model contribute suppressive signals that appear to have a predominantly
divisive influence on neuronal firing. Suppressive signals are especially
potent in direction-selective cells, where they reduce responses to
stimuli moving in the non-preferred direction.
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