Temporal pattern generation based on anticipation

A neural network model of complex temporal pattern generation is proposed and investigated analytically and by computer simulation. Temporal pattern generation is based on recognition of the contexts of individual components. Based an its acquired experience the model actively yields system anticipation, which then compares with the actual input flow. A mismatch triggers self-organization of context learning, which ultimately leads to resolving various ambiguities in producing complex temporal patterns. We show analytically that the network model can learn to generate any complex temporal pattern. Multiple patterns can be acquired sequentially by the system, manifesting a form of retroactive interference. The model is consistent with cognitive studies of sequential learning