Dasgupta, Chandan (1992) Neural network modeling of associative memory: beyond the Hopfield model Physica A: Statistical and Theoretical Physics, 186 (1-2). pp. 49-60. ISSN 0378-4371
Full text not available from this repository.
Official URL: http://linkinghub.elsevier.com/retrieve/pii/037843...
Related URL: http://dx.doi.org/10.1016/0378-4371(92)90364-V
Abstract
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: | Article |
---|---|
Source: | Copyright of this article belongs to Elsevier Science. |
ID Code: | 22886 |
Deposited On: | 25 Nov 2010 13:54 |
Last Modified: | 25 Nov 2010 13:54 |
Repository Staff Only: item control page