An associative memory for the on-line recognition and prediction of temporal sequences
| dc.creator | Bose, J. | |
| dc.creator | Furber, S. B. | |
| dc.creator | Shapiro, J. L. | |
| dc.date | 2006-11-05 | |
| dc.date.accessioned | 2026-07-07T07:31:39Z | |
| dc.date.available | 2026-07-07T07:31:39Z | |
| dc.description | 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. | |
| dc.description | Published in IJCNN 2005, Montreal, Canada | |
| dc.identifier | https://arxiv.org/abs/cs/0611020 | |
| dc.identifier | http://arxiv.org/abs/cs/0611020 | |
| dc.identifier | doi:10.1109/IJCNN.2005.1556028 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/118828 | |
| dc.subject | Neural and Evolutionary Computing | |
| dc.subject | Artificial Intelligence | |
| dc.title | An associative memory for the on-line recognition and prediction of temporal sequences | |
| dc.type | text |