CAREER: Robust Identification and Multi-Objective Control Methods for Neuronal Networks Under Uncertainty

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Overview: The control of biological neural networks underpins the development of minimally-invasive brain therapies as well as adaptive learning for cyber-physical systems, but it remains challenging because of the irregular dynamics involved. These systems also have sparse and weak connections and a range of dynamics that cannot be fully probed. There is an urgent need to determine the impact of unmodeled dynamics on the controllability of these networks and develop robust controls accordingly, otherwise controllers will remain underperforming, fragile, and hard to calibrate. This is the case for deep brain stimulation (DBS), which follows a conservative "one-size-fits-all" paradigm and remains underutilized despite having the potential to treat millions of people worldwide. The objective of this CAREER program is to develop identification methods that estimate the impact of unmodeled dynamics on neuronal circuits and a robust control framework for these circuits. Brain circuits targeted by Parkinson's disease and DBS will be considered to maximize the impact of the research. The work will be paired with educational plans that address current limitations in the training of neural engineers and broaden the presence of first-generation college students in STEM.Intellectual Merits: This research will fill critical gaps in the knowledge base that provides linkage between global dynamics of a neural network and dynamics of individual neurons under control. It will also contribute a robust control framework for neural populations and brain circuits. Educational activities will fill critical gaps in the training of neural engineers by integrating modeling and control in the design process of neural prostheses. Applied to DBS, this research will help personalize DBS to PD populations who are currently excluded from this treatment, thus enabling new options for chronically ill patients.Broader Impacts: The research will benefit the well-being of Parkinson's disease patients, including patients who are now excluded from DBS. The training of neural engineers will also be improved, thus helping the formation of a better-trained and more globally-competitive workforce. The integration of research and outreach will finally create a pipeline to attract high-school students towards STEM fields and facilitate the learning of engineering principles at the pre-college level.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.