Rapid learning and robust recall of long sequences in modular associator networks

Biologically inspired neural networks which perform temporal sequence learning and generation are frequently based on hetero-associative memories. Recent work by Jensen and Lisman has suggested that a model which connects an auto-associator module to a hetero-associator module can perform this function. We modify this architecture in a simplified model which in contrast uses a pair of connected auto-associative networks with hetero-associatively trained synapses in one of the paths between them. We simulate both models, finding that accurate and robust recall of learned sequences can easily be performed with the modified model introduced here, strongly outperforming the previous architecture.