Iterative learning control of neuronal firing based on FHN and HR models.
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 fa...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0329380 |
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| author | Chunhua Yuan Xiaotong Wang Xiangyu Li Yueyang Zhao |
| author_facet | Chunhua Yuan Xiaotong Wang Xiangyu Li Yueyang Zhao |
| author_sort | Chunhua Yuan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-93e8f89e735940fda8b7d402eb2fe3a6 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-93e8f89e735940fda8b7d402eb2fe3a62025-08-20T03:39:10ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01207e032938010.1371/journal.pone.0329380Iterative learning control of neuronal firing based on FHN and HR models.Chunhua YuanXiaotong WangXiangyu LiYueyang ZhaoNeuronal 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.https://doi.org/10.1371/journal.pone.0329380 |
| spellingShingle | Chunhua Yuan Xiaotong Wang Xiangyu Li Yueyang Zhao Iterative learning control of neuronal firing based on FHN and HR models. PLoS ONE |
| title | Iterative learning control of neuronal firing based on FHN and HR models. |
| title_full | Iterative learning control of neuronal firing based on FHN and HR models. |
| title_fullStr | Iterative learning control of neuronal firing based on FHN and HR models. |
| title_full_unstemmed | Iterative learning control of neuronal firing based on FHN and HR models. |
| title_short | Iterative learning control of neuronal firing based on FHN and HR models. |
| title_sort | iterative learning control of neuronal firing based on fhn and hr models |
| url | https://doi.org/10.1371/journal.pone.0329380 |
| work_keys_str_mv | AT chunhuayuan iterativelearningcontrolofneuronalfiringbasedonfhnandhrmodels AT xiaotongwang iterativelearningcontrolofneuronalfiringbasedonfhnandhrmodels AT xiangyuli iterativelearningcontrolofneuronalfiringbasedonfhnandhrmodels AT yueyangzhao iterativelearningcontrolofneuronalfiringbasedonfhnandhrmodels |