Abstract:
OBJECTIVE:Neural modulation is a fundamental tool for understanding and treating neurological and psychiatric diseases. However, due to the high-dimensional space, subject-specific responses, and variability within each subject, it is a major challenge to select the stimulation parameters that have the desired effect. Data-driven optimization provides a range of different algorithms and tools for addressing this challenge, but each of these algorithms has specific strengths and limitations, and therefore must be carefully designed for a given neural modulation problem. Here we present a powerful and unifying framework for designing data-driven optimization algorithms for neural modulation. APPROACH:We develop this framework using an optogenetic medial septum stimulation model, where the goal is to find the stimulation parameters that modulate hippocampal gamma power to a desired value. This framework proceeds in three steps: 1) collecting stimulation data to create high-throughput simulation models, 2) prototyping a range of different data-driven optimization algorithms and evaluating their performance, and 3) deploying the best performing algorithm in vivo. MAIN RESULTS:Following this framework, we prototype and design an algorithm specifically for finding the medial septum optogenetic stimulation parameters that maximize hippocampal gamma power. Building on this, we then change our objective function to find the stimulation parameters that modulate gamma to a specific setpoint, use the framework to understand and anticipate the results before deploying in vivo. SIGNIFICANCE:We show that this framework can be used to design effective optimization solutions for specific neural modulation problems, and can be applied well beyond the optogenetic medial septum stimulation model.
journal_name
J Neural Engjournal_title
Journal of neural engineeringauthors
Connolly MJ,Park SE,Laxpati NG,Zaidi SA,Ghetiya M,Fernandez A,Gutekunst CA,Gross REdoi
10.1088/1741-2552/abd048subject
Has Abstractpub_date
2020-12-03 00:00:00eissn
1741-2560issn
1741-2552pub_type
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