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Neural network modeling of associative memory: Beyond the Hopfield model

Chandan, Dasgupta (1992) Neural network modeling of associative memory: Beyond the Hopfield model. In: Physica A: Statistical and Theoretical Physics, 186 (1-2). pp. 49-60.

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Official URL: http://dx.doi.org/10.1016/0378-4371(92)90364-V


A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying dynamics are used to store and associatively recall information, are described. In the first class of models, a hierarchical structure is used to store an exponentially large number of strongly correlated memories. The second class of models uses limit cycles to store and retrieve individual memories. A neurobiologically plausible network that generates low-amplitude periodic variations of activity, similar to the oscillations observed in electroencephalographic recordings, is also described. Results obtained from analytic and numerical studies of the properties of these networks are discussed.

Item Type: Journal Article
Publication: Physica A: Statistical and Theoretical Physics
Publisher: Elsevier science
Additional Information: Copyright of this article belongs to Elsevier science.
Department/Centre: Division of Physical & Mathematical Sciences > Physics
Date Deposited: 05 May 2011 05:25
Last Modified: 05 May 2011 05:25
URI: http://eprints.iisc.ac.in/id/eprint/37266

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