Modeling temporal response characteristics of V1 neurons with a dynamic normalization model

We present a dynamic normalization model to characterize both the transient and the steady-state components of V1 simple and complex cell responses. P rimary receptive field properties are chiefly determined by the convergence of LGN afferents. These linear responses are rectified, and subjected to s hunting inhibition through cortical feedback, which accounts for the non-li near characteristics of the neuronal responses. The duration of the transie nt response is determined by the time delay and the low-pass filtering of t he cortical feedback. In addition to accounting for basic non-linear behavi ors such as response saturation and cross-orientation inhibition, the model is also able to reproduce several short-term contrast and pattern-selectiv e adaptation effects. (C) 2001 Published by Elsevier Science B.V.