ISSN:
1573-773X
Keywords:
associative memory
;
dynamical systems
;
Glauber dynamics
;
Hopfield model
;
infinite dimensional state space
;
stability
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
Abstract A generalization of the Little–Hopfield neural network model for associative memories is presented that considers the case of a continuum of processing units. The state space corresponds to an infinite dimensional euclidean space. A dynamics is proposed that minimizes an energy functional that is a natural extension of the discrete case. The case in which the synaptic weight operator is defined through the autocorrelation rule (Hebb rule) with orthogonal memories is analyzed. We also consider the case of memories that are not orthogonal. Finally, we discuss the generalization of the non deterministic, finite temperature dynamics.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1023/A:1009689025427
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