ArticleJournal of mathematical biology2020
Optimal open-loop desynchronization of neural oscillator populations.
Article in Journal of mathematical biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 14 citations in OpenAlex.
- Control of Oscillator Networks with Mean-Field Measurement: A Hybrid Open/Closed-Loop Approach.IEEE transactions on control systems technology : a publication of the IEEE Control Systems Society · 2026Article
- In vivo efficacy of current stimuli computed by optimal tracking of neuron membrane voltage.PloS one · 2026Article
- Magnitude-constrained optimal chaotic desynchronization of neural populations.Frontiers in network physiology · 2025Article
- The influence of synaptic plasticity on critical coupling estimates for neural populations.Journal of mathematical biology · 2024Article
- Analysis of neural clusters due to deep brain stimulation pulses.Biological cybernetics · 2020Article
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Authors and funding
1 author at 1 institution in 1 country.
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Abstract
Deep brain stimulation (DBS) is an increasingly used medical treatment for various neurological disorders. While its mechanisms are not fully understood, experimental evidence suggests that through application of periodic electrical stimulation DBS may act to desynchronize pathologically synchronized populations of neurons resulting desirable changes to a larger brain circuit. However, the underlying mathematical mechanisms by which periodic stimulation can engender desynchronization in a coupled population of neurons is not well understood. In this work, a reduced phase-amplitude reduction framework is used to characterize the desynchronizing influence of periodic stimulation on a population of coupled oscillators. Subsequently, optimal control theory allows for the design of periodic, open-loop stimuli with the capacity to destabilize completely synchronized solutions while simultaneously stabilizing rotating block solutions. This framework exploits system nonlinearities in order to strategically modify unstable Floquet exponents. In the limit of weak neural coupling, it is shown that this method only requires information about the phase response curves of the individual neurons. The effects of noise and heterogeneity are also considered and numerical results are presented. This framework could ultimately be used to inform the design of more efficient deep brain stimulation waveforms for the treatment of neurological disease.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.