Anticipation-based temporal pattern generation

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 on 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. The architecture of the model incorporates a short term memory for building associations between remote components and recurrent connections for self-organization and component generation in a temporal pattern. Synaptic modification is based on a one-shot normalized Hebbian rule, which is shown to exhibit temporal masking. The major conclusion, namely the network model which can learn to generate any complex temporal pattern, is established analytically. An estimate on the efficiency of the training algorithm is provided. Multiple temporal patterns can be incrementally acquired by the system, exhibiting a form of retroactive interference. Neural and cognitive plausibility of the model is discussed