Mean-field Oscillator Games with Application to Thalamocortical Network Dynamics

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The research objective of this award is to explore phase transition and self-organization in large population dynamic systems. The thrust of the methodological work is at the intersection of the dynamical systems theory and game theory. The application focus is on investigating plasticity in the thalamocortical neuronal network in brain. The research will develop methods that are general enough to apply for mean-field games and that can be exploited in diverse applications. The research approach progresses from development of theoretical tools to their specialized application to thalamocortical network modeling. Deliverables include methods and computational tools to investigate phase transitions and stability in coupled oscillator models; algorithms for adaptation in such models; and applications of these tools to reduced order modeling of thalamocortical networks; and to development of specialized network adaptation schemes for plasticity therein.If successful, the results of this research can lead to fundamental advances in our understanding of the emergence of complex behavior in a large population of dynamically interacting agents. For biological systems, this methodology offers new organizing principles to address longstanding questions concerning collective emergent behavior in terms of generalizations of game theoretic concepts. A specific research outcome concerns mechanisms for plasticity (learning) in neuronal networks. This project has put together several educational initiatives to help disseminate the methods, outcomes, and the interdisciplinary synergies. This project seeks to develop and maintain strong undergraduate involvement in research, interdisciplinary outreach activities, exposure of the engineering undergraduates to the central role of dynamics and control in applications, and foster symbiotic relationship between neuroscience and engineering.