| Modeling learnable electrical synapse for high precision spatio-temporal recognition | |
| Wu, Zhenzhi1; Zhang, Zhihong2; Gao, Huanhuan2; Qin, Jun2; Zhao, Rongzhen1; Zhao, Guangshe2; Li, Guoqi3,4 | |
| Source Publication | NEURAL NETWORKS
 |
| ISSN | 0893-6080 |
| 2022-05-01 | |
| Volume | 149Pages:184-194 |
| Corresponding Author | Li, Guoqi(guoqi.li@ia.ac.cn) |
| Abstract | Bio-inspired recipes are being introduced to artificial neural networks for the efficient processing of spatio-temporal tasks. Among them, Leaky Integrate and Fire (LIF) model is the most remarkable one thanks to its temporal processing capability, lightweight model structure, and well investigated direct training methods. However, most learnable LIF networks generally take neurons as independent individuals that communicate via chemical synapses, leaving electrical synapses all behind. On the contrary, it has been well investigated in biological neural networks that the inter-neuron electrical synapse takes a great effect on the coordination and synchronization of generating action potentials. In this work, we are engaged in modeling such electrical synapses in artificial LIF neurons, where membrane potentials propagate to neighbor neurons via convolution operations, and the refined neural model ECLIF is proposed. We then build deep networks using ECLIF and trained them using a back-propagation-through-time algorithm. We found that the proposed network has great accuracy improvement over traditional LIF on five datasets and achieves high accuracy on them. In conclusion, it reveals that the introduction of the electrical synapse is an important factor for achieving high accuracy on realistic spatio-temporal tasks. |
| Keyword | Electrical synapse coupling Leaky-integrate-and-fire model Spatio-temporal information Bio-plausible neuronal dynamics |
| DOI | 10.1016/j.neunet.2022.02.006 |
| WOS Keyword | SPIKING NEURONS ; FIRE MODEL ; BACKPROPAGATION ; MECHANISMS ; NETWORKS ; DYNAMICS |
| Indexed By | SCI |
| Language | 英语 |
| WOS Research Area | Computer Science ; Neurosciences & Neurology |
| WOS Subject | Computer Science, Artificial Intelligence ; Neurosciences |
| WOS ID | WOS:000793060100014 |
| Publisher | PERGAMON-ELSEVIER SCIENCE LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/49402 |
| Collection | 数字内容技术与服务研究中心_听觉模型与认知计算 |
| Corresponding Author | Li, Guoqi |
| Affiliation | 1.Lynxi Technol, Beijing 100097, Peoples R China 2.Xi An Jiao Tong Univ, Sch Automation Sci & Engn, Xian 710049, Shaanxi, Peoples R China 3.Chinese Acad Sci, Inst Automaton, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100190, Peoples R China |
| Corresponding Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Recommended Citation GB/T 7714 | Wu, Zhenzhi,Zhang, Zhihong,Gao, Huanhuan,et al. Modeling learnable electrical synapse for high precision spatio-temporal recognition[J]. NEURAL NETWORKS,2022,149:184-194. |
| APA | Wu, Zhenzhi.,Zhang, Zhihong.,Gao, Huanhuan.,Qin, Jun.,Zhao, Rongzhen.,...&Li, Guoqi.(2022).Modeling learnable electrical synapse for high precision spatio-temporal recognition.NEURAL NETWORKS,149,184-194. |
| MLA | Wu, Zhenzhi,et al."Modeling learnable electrical synapse for high precision spatio-temporal recognition".NEURAL NETWORKS 149(2022):184-194. |
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