Scene Segmentation through Synchronization

Even when creating a biologically realistic model for an apparently very simple cognitive task such as seeking a certain object in the visual field, we are confronted with severe problems concerning the binding of distributed representations. In this work, we present simulation results from a model of two reciprocally coupled visual cortical areas. One area is a peripheral visual area where local object features are represented; the other is a more central visual area where whole objects are recognized. In our model, correct binding is achieved by the simultaneous switching of the activation state of corresponding neuron groups. We relate our simulations to neurophysiological findings concerning attention and biased competition and demonstrate how these findings can be explained very naturally by assuming different kinds of bindings between neuron groups in different areas as produced by our model. Although the binding is fluctuating in the absence of attention, it becomes static by the attentional bias.

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