Feature Binding Through Synchronized Neuronal Oscillations: A Preliminary Study

In this report we analyze the feature binding problem, a combinatorial complexity problem that affects connectionist networks using multiple topographic representations of an image. Inspired from some evidence about the human visual system, we suggest that a solution to this problem may derive by the combined use of attention mechanisms and by exploiting the temporal synchrony of neuronal firing. To this end, a new framework is proposed in terms of a neuronal model, and of a computational architecture capable of producing synchronized firing in distributed assemblies of neurons. This synchronized behavior only affects neurons selected by the network to represent objects of interest. The architecture is structured into a set of feature, conspicuity, and saliency maps, whose neurons are connected in a feedback loop. A number of mechanisms are proposed in order to implement each of these stages, including strategies for reinforcing the synchronous firing of the selected neurons.

ICSI Technical Report TR-94-044

Technical Report