A two layer disparity selective simple cell model

The responses of disparity selective complex cells in the mammalian visual system are often modeled by the disparity energy model. This model linearly combines inputs from binocular simple cell units, whose responses are computed by the combination of left and right eye inputs through linear binocular receptive fields, followed by half wave rectification and an expansive nonlinearity. While many complex cells have responses that can be modeled by the standard disparity energy model, there are many that cannot be. While the standard disparity energy model has been extended to account for some specific types of tuning (e.g. cells that are “monocularly responsive” in that they only respond to input from one eye, yet are disparity tuned indicating that they receive input from both eyes), actual neurons display a wider range of ocular dominance indices and disparity selectivities than can be fully accounted for by these models. Here, we describe a two layer simple cell model that can be used to construct complex cells that fully cover this range. The model combines the responses from a first layer of model monocular simple cells. By adjusting the weights from the monocular to a second binocular layer, the model can exhibit more diverse tuning properties than previously proposed models. We also show that these weights can be learned by sparse coding, and that if so, there is a strong relationship between distribution of tuning properties in the population and the input disparity statistics.