Yuan Chunhua, Wang Xiaotong, Li Xiangyu, Zhao Yueyang
School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang, China.
Library, Shengjing Hospital of China Medical University, Shenyang, China.
PLoS One. 2025 Jul 31;20(7):e0329380. doi: 10.1371/journal.pone.0329380. eCollection 2025.
Neuronal firing patterns are fundamental to neural information processing and functional regulation, with abnormal firing closely linked to a range of neurological disorders. However, existing neuromodulation techniques largely rely on open-loop stimulation strategies, which lack adaptability and fail to provide precise control over neuronal dynamics. To address this limitation, this study introduces a novel iterative learning control (ILC) framework based on proportional-integral (PI) control for closed-loop modulation of neuronal firing patterns. The proposed method is developed and validated using two representative neuron models: the FitzHugh-Nagumo (FHN) and Hindmarsh-Rose (HR) models. A dynamical analysis of these models is conducted, followed by the design and implementation of a PI-based ILC strategy. Numerical simulations demonstrate that the proposed control method significantly outperforms conventional PI control, achieving lower tracking errors, enhanced control accuracy, and improved system stability. Additionally, the ILC approach exhibits strong adaptability to different neuronal dynamics, highlighting its potential for precise and robust regulation in complex neural systems. These findings offer a theoretical basis for advancing closed-loop neuromodulation technologies, with promising implications for applications in neurorehabilitation and the treatment of neurological disorders.
神经元放电模式是神经信息处理和功能调节的基础，异常放电与一系列神经疾病密切相关。然而，现有的神经调节技术很大程度上依赖于开环刺激策略，这种策略缺乏适应性，无法对神经元动态进行精确控制。为了解决这一局限性，本研究引入了一种基于比例积分（PI）控制的新型迭代学习控制（ILC）框架，用于神经元放电模式的闭环调制。所提出的方法是使用两个具有代表性的神经元模型开发并验证的：FitzHugh-Nagumo（FHN）模型和Hindmarsh-Rose（HR）模型。对这些模型进行了动力学分析，随后设计并实施了基于PI的ILC策略。数值模拟表明，所提出的控制方法明显优于传统的PI控制，实现了更低的跟踪误差、更高的控制精度和更好的系统稳定性。此外，ILC方法对不同的神经元动态表现出很强的适应性，突出了其在复杂神经系统中进行精确和稳健调节的潜力。这些发现为推进闭环神经调节技术提供了理论基础，对神经康复和神经疾病治疗的应用具有广阔的前景。
