Inference and segmentation in cortical processing

We present a modelling framework for cortical processing aimed at understanding how, maintaining biological plausibility, neural network models can: (a) approximate general inference algorithms like belief propagation, combining bottom-up and top-down information, (b) solve Rosenblatt's classical superposition problem, which we link to the binding problem, and (c) do so based on an unsupervised learning approach. The framework leads to two related models: the first model shows that the use of top-down feedback significantly improves the network's ability to perform inference of corrupted inputs; the second model, including oscillatory behavior in the processing units, shows that the superposition problem can be efficiently solved based on the unit's phases.