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

We present a dynamic normalization model to represent both the transient and the steady state components of V1 simple and complex cell responses. Primary receptive eld properties are chie y determined by the convergence of LGN aerents. These linear responses are rectied, and subjected to shunting inhibition through cortical feedback, which accounts for the non-linear characteristics of the neuronal responses. The duration of the transient response is determined by the time delay and the low-pass ltering of the cortical feedback. In addition to accounting for basic nonlinear behaviors such as response saturation and cross-orientation inhibition, the model is also able to reproduce several short-term contrast and pattern-selective adaptation eects