2 papers
cs.LG2026
Neuronal Self-Adaptation Enhances Capacity and Robustness of Representation in Spiking Neural Networks
Zhuobin Yang, Yeyao Bao, Liangfu Lv +3
Spiking Neural Networks (SNNs) are promising for energy-efficient, real-time edge computing, yet their performance is often constrained by the limited adaptability of conventional…
cs.LG2026
CDRL: A Reinforcement Learning Framework Inspired by Cerebellar Circuits and Dendritic Computational Strategies
Sibo Zhang, Rui Jing, Liangfu Lv +2
Reinforcement learning (RL) has achieved notable performance in high-dimensional sequential decision-making tasks, yet remains limited by low sample efficiency, sensitivity to nois…