Tuning Synaptic Connections Instead of Weights by Genetic Algorithm in Spiking Policy Network
文献类型：期刊论文
| 作者 | Duzhen Zhang1,2 ; Tielin Zhang1,2,3; Shuncheng Jia1,2; Qingyu Wang1,2; Bo Xu1,2,3
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| 刊名 | Machine Intelligence Research
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| 出版日期 | 2024 |
| 卷号 | 21期号:5页码:906-918 |
| 关键词 | Spiking neural networks genetic evolution bio-inspired learning agent & cognitive architectures robotic control |
| ISSN号 | 2731-538X |
| DOI | 10.1007/s11633-023-1481-1 |
| 英文摘要 | Learning from interaction is the primary way that biological agents acquire knowledge about their environment and themselves. Modern deep reinforcement learning (DRL) explores a computational approach to learning from interaction and has made significant progress in solving various tasks. However, despite its power, DRL still falls short of biological agents in terms of energy efficiency. Although the underlying mechanisms are not fully understood, we believe that the integration of spiking communication between neurons and biologically-plausible synaptic plasticity plays a prominent role in achieving greater energy efficiency. Following this biological intuition, we optimized a spiking policy network (SPN) using a genetic algorithm as an energy-efficient alternative to DRL. Our SPN mimics the sensorimotor neuron pathway of insects and communicates through event-based spikes. Inspired by biological research showing that the brain forms memories by creating new synaptic connections and rewiring these connections based on new experiences, we tuned the synaptic connections instead of weights in the SPN to solve given tasks. Experimental results on several robotic control tasks demonstrate that our method can achieve the same level of performance as mainstream DRL methods while exhibiting significantly higher energy efficiency. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/59421]  |
| 专题 | 自动化研究所_学术期刊_International Journal of Automation and Computing |
| 作者单位 | 1.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China 2.Institute of Automation, Chinese Academy of Sciences (CAS), Beijing 100190, China 3.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences (CAS), Shanghai 200031, China |
| 推荐引用方式 GB/T 7714 | Duzhen Zhang, Tielin Zhang, Shuncheng Jia,et al. Tuning Synaptic Connections Instead of Weights by Genetic Algorithm in Spiking Policy Network[J]. Machine Intelligence Research,2024,21(5):906-918. |
| APA | Duzhen Zhang, Tielin Zhang, Shuncheng Jia, Qingyu Wang,& Bo Xu.(2024).Tuning Synaptic Connections Instead of Weights by Genetic Algorithm in Spiking Policy Network.Machine Intelligence Research,21(5),906-918. |
| MLA | Duzhen Zhang,et al."Tuning Synaptic Connections Instead of Weights by Genetic Algorithm in Spiking Policy Network".Machine Intelligence Research 21.5(2024):906-918. |
入库方式： OAI收割
来源：自动化研究所
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