Learning and Controlling Multiscale Dynamics in Spiking Neural Networks Using Recursive Least Square Modifications
文献类型：期刊论文
| 作者 | Wei, Qinglai2,3,4 ; Han, Liyuan2,3,4; Zhang, Tielin1,3
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| 刊名 | IEEE TRANSACTIONS ON CYBERNETICS
 |
| 出版日期 | 2024-01-09 |
| 页码 | 14 |
| 关键词 | Task analysis Neurons Trajectory Behavioral sciences Dynamic programming Control theory Biology Direct dynamic programming (DDP) Lorenz system multiscale dynamics point-to-point control recursive least square (RLS) spiking neural network (SNN) |
| ISSN号 | 2168-2267 |
| DOI | 10.1109/TCYB.2023.3343430 |
| 通讯作者 | Zhang, Tielin(tielin.zhang@ia.ac.cn) |
| 英文摘要 | Invasive brain-computer interfaces (BCIs) have the capability to simultaneously record discrete signals across multiple scales, but how to effectively process and analyze these potentially related signals remains an open challenge. This article introduces an innovative approach that merges modern control theory with spiking neural networks (SNNs) to bridge the gap among multiscale discrete information. Specifically, the macroscopic point-to-point trajectory is formulated as an optimal control problem with fixed terminal time and state, and it is iteratively solved using the direct dynamic programming (DDP) algorithm. Additionally, SNN is utilized to simulate microscale neural activities in the premotor cortex, employing the product of the weighted adjacency matrix and the mesoscale firing rate to approximate the macroscopic trajectory. The error between actual macroscale behavior and the preceding approximation is then used to update the weighted adjacency matrix through the recursive least square (RLS) method. Analysis and simulation of various tasks, including low-dimensional point-to-point tasks, high-dimensional complex Lorenz systems, and center-out-and-back tasks, verify the feasibility and interpretability of our method in processing multiscale signals ranging from spiking neurons to motion trajectory through the integration of SNN and control theory. |
| WOS关键词 | CORTEX |
| 资助项目 | National Key Research and Development Program of China |
| WOS研究方向 | Automation & Control Systems ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001166482100001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/55640]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Zhang, Tielin |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Lab Cognit & Decis Intelligence Complex Syst, Beijing 100190, Peoples R China 2.Macau Univ Sci & Technol, Inst Syst Engn, Macau, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wei, Qinglai,Han, Liyuan,Zhang, Tielin. Learning and Controlling Multiscale Dynamics in Spiking Neural Networks Using Recursive Least Square Modifications[J]. IEEE TRANSACTIONS ON CYBERNETICS,2024:14. |
| APA | Wei, Qinglai,Han, Liyuan,&Zhang, Tielin.(2024).Learning and Controlling Multiscale Dynamics in Spiking Neural Networks Using Recursive Least Square Modifications.IEEE TRANSACTIONS ON CYBERNETICS,14. |
| MLA | Wei, Qinglai,et al."Learning and Controlling Multiscale Dynamics in Spiking Neural Networks Using Recursive Least Square Modifications".IEEE TRANSACTIONS ON CYBERNETICS (2024):14. |
入库方式： OAI收割
来源：自动化研究所
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