15 citations · 23 across the 14 of their papers we have counts for
15 papers
FidLIF: Parallel Training of High-Fidelity Spiking Neurons with Exact BPTT
Kaixiang He, Yujie Wu
Although spiking neural networks (SNNs) show promise in energy-efficient computing through event-driven computation and stateful temporal processing, their recurrent membrane dynam…
PLS-Calib: A Partial Least Squares Framework for Event Camera and Odometry Calibration under Ground Motion Constraints
Guangyu Li, Xiao Li, Yujie Wu +5
Accurate extrinsic rotation calibration between sensors is fundamental to the performance of robotic perception systems. However, most existing calibration techniques rely on full…
E-TraMamba: A New Paradigm for Efficient Long-Term 3D Feature Tracking with Event Cameras
Juwei Shen, Yujie Wu, Changwen Chen
Event-based 3D tracking enables low-latency and high-speed perception, while existing CNN- and Transformer-based trackers struggle to capture long-range spatiotemporal dependencies…
Dendritic In-Context Learning in a Single-Layer Spiking Neural Network
Juwei Shen, Yujie Wu, Changwen Chen
In-context learning (ICL) operates via implicit gradient descent embedded in the forward pass of modern AI architectures -- Transformers, Mamba, state-space models, and MLPs. Captu…
BioMedVR: Confusion-Aware Mixture-of-Prompt Experts for Biomedical Visual Reprogramming
Jiaxiang Liu, Tianxiang Hu, Juwei Guan +5
Recent advances in vision-language models (VLMs) such as CLIP have demonstrated strong generalization across natural-image domains. However, adapting these models to biomedical ima…
A Theory-grounded Hybrid Neural Network Integrating Complementary Estimation Mechanisms for Stable Visual Object TrackingA
Yancheng Zhou, Hanle Zheng, Lei Deng +1
Hybrid neural networks (HNNs) that integrate artificial neural networks (ANNs) with brain-inspired neural networks have achieved broad success across perception and control tasks.…
MorphSNN: Adaptive Graph Diffusion and Structural Plasticity for Spiking Neural Networks
Yongsheng Huang, Peibo Duan, Yujie Wu +7
Spiking Neural Networks (SNNs) currently face a critical bottleneck: while individual neurons exhibit dynamic biological properties, their macro-scopic architectures remain confine…
BrainFuse: a unified infrastructure integrating realistic biological modeling and core AI methodology
Baiyu Chen, Yujie Wu, Siyuan Xu +9
Neuroscience and artificial intelligence represent distinct yet complementary pathways to general intelligence. However, amid the ongoing boom in AI research and applications, the…
Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects
Chenxiang Ma, Xinyi Chen, Yanchen Li +7
Temporal processing is fundamental for both biological and artificial intelligence systems, as it enables the comprehension of dynamic environments and facilitates timely responses…
Distance-Forward Learning: Enhancing the Forward-Forward Algorithm Towards High-Performance On-Chip Learning
Yujie Wu, Siyuan Xu, Jibin Wu +4
The Forward-Forward (FF) algorithm was recently proposed as a local learning method to address the limitations of backpropagation (BP), offering biological plausibility along with…
PMSN: A Parallel Multi-compartment Spiking Neuron for Multi-scale Temporal Processing
Xinyi Chen, Jibin Wu, Chenxiang Ma +4
Spiking Neural Networks (SNNs) hold great potential to realize brain-inspired, energy-efficient computational systems. However, current SNNs still fall short in terms of multiscale…
Unveiling the Potential of Spiking Dynamics in Graph Representation Learning through Spatial-Temporal Normalization and Coding Strategies
Mingkun Xu, Huifeng Yin, Yujie Wu +5
In recent years, spiking neural networks (SNNs) have attracted substantial interest due to their potential to replicate the energy-efficient and event-driven processing of biologic…
Advancing Spiking Neural Networks towards Deep Residual Learning
Yifan Hu, Lei Deng, Yujie Wu +2
Despite the rapid progress of neuromorphic computing, inadequate capacity and insufficient representation power of spiking neural networks (SNNs) severely restrict their applicatio…
H2Learn: High-Efficiency Learning Accelerator for High-Accuracy Spiking Neural Networks
Ling Liang, Zheng Qu, Zhaodong Chen +6
Although spiking neural networks (SNNs) take benefits from the bio-plausible neural modeling, the low accuracy under the common local synaptic plasticity learning rules limits thei…
Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning
Mingkun Xu, Yujie Wu, Lei Deng +3
Biological spiking neurons with intrinsic dynamics underlie the powerful representation and learning capabilities of the brain for processing multimodal information in complex envi…