Recurrent Oscillatory Self-Organizing Map: Learning and Entrainment to Multiple Periodicities

Eighth Midwest Artificial intelligence and Cognitive Science Conference

Volume

Eighth Midwest Artificial intelligence and Cognitive Science Conference

Contents

A recurrent oscillatory self-organizing map (ROSOM) introduced. This is an architecture that assumes oscillatory states, assigned to all units, indicating their "readiness-tofire" or "exhaustion", and constitutes an internal timing mechanism. The architecture feeds the vector of all these states back into the input layer, making the units recognize and adapt to not only input but also to the entire history of activations. The self-organized map, so equipped, is capable of detecting sequences that are consistent over time in the input flow. The timing mechanism translates these sequences into real-time periodicities to which the model can automatically entrain itself. The network is shown to distinguish between sequence endpoints that differ only with respect to their temporal context, and to entrain to the periodicity of simple data, provided that the initial wavelength is set close enough to one of the data’s salient periodicities. Furthermore, the network has, in principle, the capability of carrying on activity in the absence of any input.