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

J. Bose·S. B. Furber·J. L. Shapiro
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Abstract

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 with 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.

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J. Bose, S. B. Furber, J. L. Shapiro. 2006-11-05. An associative memory for the on-line recognition and prediction of temporal sequences. https://doi.org/10.1109/ijcnn.2005.1556028

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