Complex valued artificial recurrent neural network as a novel approach to model the perceptual binding problem

In this paper we suggest a new model for solving the binding problem by introducing complex-valued recurrent networks. These networks can represent sinusoidal oscillations and their phase, i.e., they can model the binding problem of neuronal assemblies by adjusting the relative phase of the oscillations of different feature detectors. As feature examples, we use color and shape – but the network would also function with any combination of other features. The suggested network architecture performs image generalization but can also be used as an image memory. The information about object color is represented in the phase of the network weights, while the spatial distribution of the neurons codes represent the object’s shape. We will show that the architecture can generalize object shapes and recognize object color with very low computational overhead.