An associative memory for the on-line recognition and prediction of temporal sequences
<|reference_start|>An associative memory for the on-line recognition and prediction of temporal sequences: 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.<|reference_end|>
@article{bose2006an,
    title={An associative memory for the on-line recognition and prediction of
  temporal sequences},
    author={J. Bose, S.B. Furber, J.L. Shapiro},
    journal={arXiv preprint arXiv:cs/0611020},
    year={2006},
    doi={10.1109/IJCNN.2005.1556028},
    archivePrefix={arXiv},
    eprint={cs/0611020},
    primaryClass={cs.NE cs.AI}
}