A multi-modular associator network for simple temporal sequence learning and generation.
Lawrence, Michael, Thomas P. Trappenberg, Alan Fine · 2005
Temporal sequence generation readily occurs in nature. For example performing a series of motor movements or recalling a sequence of episodic memories. Proposed networks which perform temporal sequence generation are often in the form of a modification to an auto-associative memory by using heteroassociative or time-varying synaptic strengths, requiring some pre-chosen temporal functions. Intra-modular synapses are trained auto-associatively with a Hebb rule, while a set of inter-module synapses are hetero-associative. Our model is compared to one by Lisman, which uses hetero-associative recurrent synapses in one of the modules, and auto-associative synapses between modules.