Computational models are crucial in understanding brain function. Their architecture is designed to replicate known brain structures, and the behavior that emerges is then compared to observed fMRI and other imaging techniques. As the models become more complex with more parameters, they can explain more of the observed phenomena, and may eventually be used for diagnosis and design of treatments of brain disorders. However, those parameters need to be carefully optimized for the models to work, which becomes intractable as the models grow. In this preliminary work, CMA-ES has been configured to optimize continuous parameters of a functiona connectivity model, resulting in a better fit to empirical data than manually selected parameters in all trial runs. This approach will be combined with other EC techniques to optimize other parameters. The techniques will be scaled up to more detailed structural and functional data and local parameters.

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In Genetic and Evolutionary Computation Conference Companion, 2019.

@inproceedings{maile:geccows19, title={Implementing Evolutionary Optimization to Model Neural Functional Connectivity}, author={Kaitlin Maile and Manish Saggar and Risto Miikkulainen}, booktitle={Genetic and Evolutionary Computation Conference Companion}, month={ }, url="http://www.cs.utexas.edu/users/ai-lab?maile:geccows19", year={2019} }