A neural model of sequential memory

A neural model for temporal pattern generation is analyzed for learning multiple complex sequences in a sequential manner. The network exhibits a degree of interference when new sequences are acquired. It is proven that the model is capable of incrementally learning a finite number of complex sequences. The model is then evaluated with a large set of highly correlated sequences. While the number of intact sequences increases linearly, the amount of retraining due to interference appears to be independent of the size of existing memory. The idea of chunking helps to substantially reduce the amount of retraining in sequential learning. The network investigated here constitutes an effective sequential memory