13 citations · 39 across the 7 of their papers we have counts for
7 papers
Spike timing reshapes robustness against attacks in spiking neural networks
Jianhao Ding, Zhaofei Yu, Tiejun Huang +1
The success of deep learning in the past decade is partially shrouded in the shadow of adversarial attacks. In contrast, the brain is far more robust at complex cognitive tasks. Ut…
ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networks
Jiangrong Shen, Qi Xu, Jian K. Liu +3
Spiking neural networks (SNNs) have manifested remarkable advantages in power consumption and event-driven property during the inference process. To take full advantage of low powe…
Biologically inspired structure learning with reverse knowledge distillation for spiking neural networks
Qi Xu, Yaxin Li, Xuanye Fang +4
Spiking neural networks (SNNs) have superb characteristics in sensory information recognition tasks due to their biological plausibility. However, the performance of some current s…
Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation
Qi Xu, Yaxin Li, Jiangrong Shen +3
Spiking neural networks (SNNs) are well known as the brain-inspired models with high computing efficiency, due to a key component that they utilize spikes as information units, clo…
Winner-Take-All as Basic Probabilistic Inference Unit of Neuronal Circuits
Zhaofei Yu, Yonghong Tian, Tiejun Huang +1
Experimental observations of neuroscience suggest that the brain is working a probabilistic way when computing information with uncertainty. This processing could be modeled as Bay…
Neural System Identification with Spike-triggered Non-negative Matrix Factorization
Shanshan Jia, Zhaofei Yu, Arno Onken +3
Neuronal circuits formed in the brain are complex with intricate connection patterns. Such complexity is also observed in the retina as a relatively simple neuronal circuit. A reti…
A simple blind-denoising filter inspired by electrically coupled photoreceptors in the retina
Yang Yue, Liuyuan He, Gan He +4
Photoreceptors in the retina are coupled by electrical synapses called "gap junctions". It has long been established that gap junctions increase the signal-to-noise ratio of photor…