The computational role of dopamine D1 receptors in working memory
Neural Networks - Computational models of neuromodulation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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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.