Evolutionary analysis of hierarchically coupled associative memories

Inspired by the theory of neuronal group selection (TNGS), we have carried out an analysis of the capacity of convergence of a multi-level associative memory based on coupled generalized-brain-state-in-a-box (GBSB) networks through evolutionary computation. The TNGS establishes that a memory process can be described as being organized, functionally, in hierarchical levels where higher levels coordinate sets of functions in the lower levels. According to this theory, the most basic units in the cortical area of the brain are called neuronal groups or first-level blocks of memories and the higher-level memories are formed through selective strengthening or weakening of the synapses amongst the neuronal groups. In order to analyse this effect, we propose that the higher levels should emerge through a learning mechanism as correlations of lower level memories. According to this proposal, this paper describes a method of acquiring the inter-group synapses based on evolutionary computation. Thus the results show that evolutionary computation is feasible as it allows the emergence of complex behaviours which could be potentially excluded in other learning methods. keywords: Associative memory, Evolutionary Computation, Generalized-brain-state-in-a-box (GBSB) model, Theory of neuronal group selection (TNGS).