Multi-objective parameter estimation of biologically plausible neural networks in different behavior stages

An essential behaviour of biological neural networks is the switching between different dynamical stages i.e. during development, learning, attention or memory formation. This seems to be a key element in understanding the balance of stability and flexibility of biological information systems and should also be implemented in biologic plausible artificial neural networks. The parameter estimation of such artificial networks to fit it to the biological behavior in the different stages is a multi-objective problem. We show a multi-population genetic algorithm to get useful parameter combinations with an adapted cross population estimation of fitness and recombination of genes. The algorithm is tested on parameter fitting of a working memory model and further application of dopamine modulated learning is discussed.