AUTHOR=Zimet Michael , Rajabi Faranak , Moehlis Jeff 

TITLE=Magnitude-constrained optimal chaotic desynchronization of neural populations

JOURNAL=Frontiers in Network Physiology

VOLUME=Volume 5 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/network-physiology/articles/10.3389/fnetp.2025.1646391

DOI=10.3389/fnetp.2025.1646391

ISSN=2674-0109

ABSTRACT=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.