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The response properties of neurons in V1 have been explored in many empirical studies and modeled in quantitative detail. However, the data are scattered across dozens of articles and the models employ idiosyncratic parameterization schemes tailored to fit specific data sets. Here we consolidate the fragmented results of the prior studies in terms of a unified model. The model takes static grayscale images as inputs and represents them as firing-rate patterns across a population of simple-and complex-cell-like units tuned for orientations and spatial frequencies. Divisive normalization (Heeger, 1992) accounts for surround suppression, cross-orientation and cross-frequency suppression, and various contrast and pedestal effects. The model parameters are specified in terms of a calibration procedure that makes them implementation-independent and facilitates the inclusion of the model into larger integrated …