# Multiobjective evolutionary algorithms to fit realistic computational models of brain networks to extensive experimental data.

**A. J. Nevado-Holgado**

*University of Oxford, UK*

In this session I will introduce the general problem of fitting a mathematical model to a wide collection of experimental data. For this I will present three different models - two of them use the mean firing rate description of neuronal firing, and the other uses a mean field approach. The first two are models of the basal ganglia, and find its application in Parkinson's disease and on deriving the connectivity between nuclei. The third one models the cortico-thalamic loop, and finds its application in Epilepsy research, where we attempt to use it to classify epileptic patients objectively and predict their appropriate medication. In all cases, we use a genetic algorithm with multiobjective optimization capabilities, which searches the parametric space of the mathematical models to find the values that best fit a wide collection of electrophysiological data.