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Weber presents a continuous Hopfield-like RNN as a model of complex cells in V1. This model receives input from a sparse coding generative Helmholtz machine, described earlier as a model of simple cells in V1, and which produces topography by coactivating neighbors in its "sleep phase". The complex cell model with its horizontal connections is trained to predict the simple cells' activations, while input images undergo small random shifts. The trained network features realistic centre-surround weight profiles (in position- and orientation-space) and sharpened orientation tuning curves.

Simple cells are sensitive to the phase of gratings, whereas complex cells are not and have larger receptive fields.

Both simple and complex cells' receptive fields can be described using difference-of-Gaussians filters.