Spiking Neural Networks (SNNs) are promising brain-inspired models known for low power consumption and superior potential for temporal processing, but identifying suitable learning mechanisms remains a challenge. Despite the presence of multiple coexisting learning strategies in the brain, current SNN training methods...
Zhi-Bin Li, Hai-Teng Wang, Yu-Zhe Liu et al.· Frontiers in Neuroscience· 1 citation
Spiking Neural Networks (SNNs) are promising brain-inspired models known for low power consumption and superior potential for temporal processing, but identifying suitable learning mechanisms remains a challenge. Despite the presence of multiple coexisting learning strategies in the brain, current SNN training methods...
Zhi-Bin Li, Hai-Teng Wang, Yu-Zhe Liu et al.· Frontiers in Neuroscience· 1 citation
The proposed parallel multi-compartment spiking neuron (PMSN) presents a promising solution to harness the computational advantages of detailed biological neurons, enabling high-performance and efficient temporal processing on neuromorphic computing systems.
Xinyi Chen, Jibin Wu, Chenxiang Ma et al.· IEEE Transactions on Neural...· 16 citations· ⚡4
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