Temporal processing is fundamental for biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses. Spiking neural networks (SNNs) are well-suited for efficient temporal processing owing to their rich neuronal dynamics and event-driven computa...
Chen-Xiang Ma, Xin-Yi Chen, Yan-Chen Li et al.· IEEE Transactions on Neural...· 0 citations
A biologically inspired training framework that incorporates multiple synergistic plasticity mechanisms for more effective SNN training that enables diverse learning algorithms to cooperatively modulate the accumulation of information, while allowing each mechanism to preserve its own relatively independent update dyna...
This work proposes a biology-motivated computational framework that incorporates multiple synergistic plasticity mechanisms for more effective SNN training and provides a general and extensible foundation for developing more powerful SNNs guided by multi-strategy brain-inspired learning.
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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
