224 citations · 570 across the 28 of their papers we have counts for
11 papers · 1 filter
SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence
Wei Fang, Yanqi Chen, Jianhao Ding +7
Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As…
Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies
Wei Fang, Zhaofei Yu, Zhaokun Zhou +5
Vanilla spiking neurons in Spiking Neural Networks (SNNs) use charge-fire-reset neuronal dynamics, which can only be simulated serially and can hardly learn long-time dependencies.…
Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks
Tong Bu, Wei Fang, Jianhao Ding +3
Spiking Neural Networks (SNNs) have gained great attraction due to their distinctive properties of low power consumption and fast inference on neuromorphic hardware. As the most ef…
Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes
Zecheng Hao, Jianhao Ding, Tong Bu +2
Spiking Neural Networks (SNNs) have attracted great attention due to their distinctive characteristics of low power consumption and temporal information processing. ANN-SNN convers…
Reducing ANN-SNN Conversion Error through Residual Membrane Potential
Zecheng Hao, Tong Bu, Jianhao Ding +2
Spiking Neural Networks (SNNs) have received extensive academic attention due to the unique properties of low power consumption and high-speed computing on neuromorphic chips. Amon…
Optimized Potential Initialization for Low-latency Spiking Neural Networks
Tong Bu, Jianhao Ding, Zhaofei Yu +1
Spiking Neural Networks (SNNs) have been attached great importance due to the distinctive properties of low power consumption, biological plausibility, and adversarial robustness.…
Pruning of Deep Spiking Neural Networks through Gradient Rewiring
Yanqi Chen, Zhaofei Yu, Wei Fang +2
Spiking Neural Networks (SNNs) have been attached great importance due to their biological plausibility and high energy-efficiency on neuromorphic chips. As these chips are usually…
Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks
Jianhao Ding, Zhaofei Yu, Yonghong Tian +1
Spiking Neural Networks (SNNs), as bio-inspired energy-efficient neural networks, have attracted great attentions from researchers and industry. The most efficient way to train dee…
Deep Residual Learning in Spiking Neural Networks
Wei Fang, Zhaofei Yu, Yanqi Chen +3
Deep Spiking Neural Networks (SNNs) present optimization difficulties for gradient-based approaches due to discrete binary activation and complex spatial-temporal dynamics. Conside…
Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks
Wei Fang, Zhaofei Yu, Yanqi Chen +3
Spiking Neural Networks (SNNs) have attracted enormous research interest due to temporal information processing capability, low power consumption, and high biological plausibility.…
CaMKII activation supports reward-based neural network optimization through Hamiltonian sampling
Zhaofei Yu, David Kappel, Robert Legenstein +3
Synaptic plasticity is implemented and controlled through over thousand different types of molecules in the postsynaptic density and presynaptic boutons that assume a staggering ar…