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Modeling the Functional Network for Spatial Navigation in the Human Brain

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A genetic algorithm for controlling an agent-based model of the functional human brain.

Karen E Joyce¹, Satoru Hayaska, Paul J Laurienti

¹Wake Forest University.

Biomedical Sciences Instrumentation

|August 1, 2012

Summary

This study uses genetic algorithms to optimize a dynamic brain model, enabling it to produce specific behaviors for studying neurological conditions and advancing artificial intelligence.

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Area of Science:

Neuroscience
Computational Neuroscience
Artificial Intelligence

Background:

A dynamic functional model of human brain connectivity was previously developed using functional magnetic resonance imaging (fMRI) data.
This model utilizes agent-based modeling to simulate complex brain dynamics.

Purpose of the Study:

To employ machine learning, specifically genetic algorithms (GAs), to efficiently navigate the model's vast parameter space.
To drive the dynamic brain model towards producing desired, physiologically relevant behaviors.

Main Methods:

A custom genetic algorithm (GA) was designed and tailored for the dynamic brain model.
Various fitness functions were evaluated to determine the most effective measure of solution suitability.
The GA's performance was validated by its ability to guide the model to generate pre-defined behaviors.

Main Results:

Genetic algorithms successfully identified optimal parameters within the model's large solution space.
An optimal fitness function was identified and validated for driving the model's behavior.
The optimized model demonstrated the capacity to produce specific, pre-defined functional brain dynamics.

Conclusions:

The optimized dynamic brain model can generate physiologically relevant outputs, aiding in the study of neurological diseases.
This approach offers a powerful tool for advancing artificial intelligence applications by simulating complex brain functions.