The Glauber dynamics of magnetic systems has been extended to the case of neural networks with a general odd response function. We have derived a set of recursion relations for the overlap parameter, noise average, and noise variance taken as macrovariables of the process describing the dynamics of associative memory. The retrieval process has been studied then for a hyperbolic tangent transfer function by the self-consistent signal to noise ratio method. The fatigue effect of the real neuron has been taken into account. The phase diagrams of the retrieval process reveal an enhanced storage capacity for a certain set of values of the parameters. Finally, a set of equations for the overlap parameters in the case of continuous asynchronous dynamics with nomonotone neurons has been analytically derived.
ASJC Scopus subject areas
- Statistical and Nonlinear Physics
- Statistics and Probability
- Condensed Matter Physics