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Inspired by phase resetting techniques in Parkinson\u2019s disease treatment, we incorporate stochasticity of the system\u2019s dynamics into deterministic models to address neural system intrinsic noise. We use an advanced computational solver for nonlinear stochastic partial differential equations to solve the stochastic Hamilton\u2013Jacobi\u2013Bellman equation via level set methods for a single neuron model; this allows us to find control inputs which drive the dynamics close to the system\u2019s phaseless set. When applied to coupled neuronal networks, these inputs achieve effective randomization of neuronal spike timing, leading to significant network desynchronization. Compared to its deterministic counterpart, our stochastic method can achieve considerable energy savings. The event-based control minimizes unnecessary charge transfer, potentially extending implanted stimulator battery life while maintaining robustness against variations in neuronal coupling strengths and network heterogeneities. These findings highlight the potential for developing energy-efficient neurostimulation techniques with implications for deep brain stimulation protocols. The presented computational framework could also be applied to other domains for which stochastic optimal control problems are prevalent.<\/jats:p>","DOI":"10.1007\/s00422-025-01007-3","type":"journal-article","created":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T03:06:52Z","timestamp":1742440012000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Optimal control for stochastic neural oscillators"],"prefix":"10.1007","volume":"119","author":[{"given":"Faranak","family":"Rajabi","sequence":"first","affiliation":[]},{"given":"Frederic","family":"Gibou","sequence":"additional","affiliation":[]},{"given":"Jeff","family":"Moehlis","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,20]]},"reference":[{"key":"1007_CR1","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.parkreldis.2016.03.020","volume":"28","author":"M Arlotti","year":"2018","unstructured":"Arlotti M, Rosa M, Marceglia S, Barbieri S, Priori A (2018) The adaptive deep brain stimulation challenge. 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