Abstract
We compare the use of Generalized Simulated Annealing (GSA) to the traditional Boltzmann Machine (BM), to model memory functioning, in a neural network model that describes conscious and unconscious processes involved in neurosis, which we proposed in earlier work. Modules corresponding to sensorial and symbolic memories interact, representing unconscious and conscious mental activity. We previously developed an algorithm, based on known microscopic mechanisms that control synaptic properties, and showed that the network self-organizes to a hierarchical, clustered structure. Some properties of the complex networks which result from this self-organization indicate that the use of GSA may be more appropriate than the BM, to model memory access mechanisms. We illustrate the model with simulations.
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