ArticleChaos, solitons, and fractals2020
Fractional order controllers increase the robustness of closed-loop deep brain stimulation systems.
Article in Chaos, solitons, and fractals, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Fractional order memcapacitive neuromorphic elements reproduce and predict neuronal function.Scientific reports · 2024Article
- Diverse electrical responses in a network of fractional-order conductance-based excitable Morris-Lecar systems.Scientific reports · 2023Article
- [Modulation of motor responses and neural activities with transcranial ultrasound stimulation based on closed-loop control].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Mathematical assessment of the dynamics of novel coronavirus infection with treatment: A fractional study.Chaos, solitons, and fractals · 2021Article
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4 authors.
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Abstract
We studied the effects of using fractional order proportional, integral, and derivative (PID) controllers in a closed-loop mathematical model of deep brain stimulation. The objective of the controller was to dampen oscillations from a neural network model of Parkinson's disease. We varied intrinsic parameters, such as the gain of the controller, and extrinsic variables, such as the excitability of the network. We found that in most cases, fractional order components increased the robustness of the model multi-fold to changes in the gains of the controller. Similarly, the controller could be set to a fixed set of gains and remain stable to a much larger range, than for the classical PID case, of changes in synaptic weights that otherwise would cause oscillatory activity. The increase in robustness is a consequence of the properties of fractional order derivatives that provide an intrinsic memory trace of past activity, which works as a negative feedback system. Fractional order PID controllers could provide a platform to develop stand-alone closed-loop deep brain stimulation systems.
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