35 citations · 47 across the 11 of their papers we have counts for
8 papers · 1 filter
Parallel Time-Aligned Spiking Self-Attention for Consistent Integer-Valued Training and Spike-Driven Inference
Peng Xue, Wei Fang, Kaiwei Che +4
Integer-valued leaky integrate-and-fire (I-LIF) neurons and spike firing approximation (SFA) reduce temporal training cost by representing spike trains as firing counts and normali…
A Self-Ensemble Inspired Approach for Effective Training of Binary-Weight Spiking Neural Networks
Qingyan Meng, Mingqing Xiao, Zhengyu Ma +3
Spiking Neural Networks (SNNs) are a promising approach to low-power applications on neuromorphic hardware due to their energy efficiency. However, training SNNs is challenging bec…
Multiplication-Free Parallelizable Spiking Neurons with Efficient Spatio-Temporal Dynamics
Peng Xue, Wei Fang, Zhengyu Ma +5
Spiking Neural Networks (SNNs) are distinguished from Artificial Neural Networks (ANNs) for their complex neuronal dynamics and sparse binary activations (spikes) inspired by the b…
Time-Evolving Dynamical System for Learning Latent Representations of Mouse Visual Neural Activity
Liwei Huang, ZhengYu Ma, Liutao Yu +2
Seeking high-quality representations with latent variable models (LVMs) to reveal the intrinsic correlation between neural activity and behavior or sensory stimuli has attracted mu…
Direct Training High-Performance Deep Spiking Neural Networks: A Review of Theories and Methods
Chenlin Zhou, Han Zhang, Liutao Yu +7
Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-te…
QKFormer: Hierarchical Spiking Transformer using Q-K Attention
Chenlin Zhou, Han Zhang, Zhaokun Zhou +7
Spiking Transformers, which integrate Spiking Neural Networks (SNNs) with Transformer architectures, have attracted significant attention due to their potential for energy efficien…
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…
Spikingformer: A Key Foundation Model for Spiking Neural Networks
Chenlin Zhou, Liutao Yu, Zhaokun Zhou +5
Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks, due to their event-driven spiking computation. However, some foundation…