An associative memory for the on-line recognition and prediction of temporal sequences [article]

J. Bose, S.B. Furber, J.L. Shapiro
2006 arXiv   pre-print
This paper presents the design of an associative memory with feedback that is capable of on-line temporal sequence learning. A framework for on-line sequence learning has been proposed, and different sequence learning models have been analysed according to this framework. The network model is an associative memory with a separate store for the sequence context of a symbol. A sparse distributed memory is used to gain scalability. The context store combines the functionality of a neural layer
more » ... a shift register. The sensitivity of the machine to the sequence context is controllable, resulting in different characteristic behaviours. The model can store and predict on-line sequences of various types and length. Numerical simulations on the model have been carried out to determine its properties.
arXiv:cs/0611020v1 fatcat:rx5veoz3wfbivchvkxdudlvvca