ORIGINAL RESEARCH article
Front. Netw. Physiol.
Sec. Networks in the Brain System
Volume 5 - 2025 | doi: 10.3389/fnetp.2025.1646391
This article is part of the Research TopicSelf-Organization of Complex Physiological Networks: Synergetic Principles and Applications — In Memory of Hermann HakenView all 12 articles
Magnitude-Constrained Optimal Chaotic Desynchronization of Neural Populations
Provisionally accepted
- 1University of California Santa Barbara, Santa Barbara, United States
- 2University of California, Santa Barbara, Santa Barbara, United States
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In this paper, we calculate magnitude-constrained optimal stimuli for desynchronizing a population of neurons by maximizing the Lyapunov exponent for the phase difference between pairs of neurons while simultaneously minimizing the energy which is used. This theoretical result informs the way optimal inputs can be designed for deep brain stimulation in cases where there is a biological or electronic constraint on the amount of current that can be applied. By exploring a range of parameter values, we characterize how the constraint magnitude affects the Lyapunov exponent and energy usage. Finally, we demonstrate the efficacy of this approach by considering a computational model for a population of neurons with repeated event-triggered optimal inputs.
Keywords: optimal control, Desynchronization, Deep Brain Stimulation, Lyapunov exponent, Network physiology
Received: 13 Jun 2025; Accepted: 29 Sep 2025.
Copyright: © 2025 Zimet, Rajabi and Moehlis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jeff Moehlis, moehlis@ucsb.edu
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