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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 PublicationNEURAL NETWORKS
Corresponding AuthorLi, Guoqi(
AbstractBio-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.
KeywordElectrical synapse coupling Leaky-integrate-and-fire model Spatio-temporal information Bio-plausible neuronal dynamics
Indexed BySCI
WOS Research AreaComputer Science ; Neurosciences & Neurology
WOS SubjectComputer Science, Artificial Intelligence ; Neurosciences
WOS IDWOS:000793060100014
Citation statistics
Document Type期刊论文
Corresponding AuthorLi, Guoqi
Affiliation1.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 AffilicationInstitute 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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