Multi-Objective Evolutionary Algorithms to Investigate Neurocomputational Issues : The Case Study of Basal Ganglia Models

Jean Liénard (1) , Agnès Guillot (1) , Benoît Girard (1)

1 ISIR - Institut des Systèmes Intelligents et de Robotique

The basal ganglia (BG) are a set of subcortical nuclei involved in action selection processes. We explore here the automatic parameterization of two models of the basal ganglia (the GPR and the CBG) using multi-objective evolutionary algorithms. We define two objective functions characterizing the supposed winner-takes-all functionality of the BG and obtain a set of solutions lying on the Pareto front for each model. We show that the CBG architecture leads to solutions dominating the GPR ones, this highlights the usefulness of the CBG additional connections with regards to the GPR. We then identify the most satisfying solutions on the fronts in terms of both functionality and plausibility. We finally define critical and indifferent parameters by analyzing their variations and values on the fronts, helping us to understand the dynamics governing the selection process in the BG models.

Domaines: Neurosciences

Jean Liénard, Agnès Guillot, Benoît Girard. Multi-Objective Evolutionary Algorithms to Investigate Neurocomputational Issues : The Case Study of Basal Ganglia Models. From animals to animats 11: Simulation of Adaptive Behavior, Aug 2010, Paris, France. pp.597-606. ⟨hal-00645915⟩