7 citations · 10 across the 13 of their papers we have counts for
14 papers
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…
FSD-VLN: Fast-Slow Dual-System Modeling for Aerial Long-Horizon Vision-Language Navigation
Xueke Zhu, Qingyan Meng, Liutao Yu +4
Vision-Language Navigation (VLN) enables UAV autonomous navigation in unknown environments by mapping language instructions to real-time visual inputs. Compared with GPS-dependent…
Hierarchical Zero-Order Optimization for Deep Neural Networks
Sansheng Cao, Zhengyu Ma, Yonghong Tian
Zeroth-order (ZO) optimization has long been favored for its biological plausibility and its capacity to handle non-differentiable objectives, yet its computational complexity has…
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…
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…
A High Energy-Efficiency Multi-core Neuromorphic Architecture for Deep SNN Training
Mingjing Li, Huihui Zhou, Xiaofeng Xu +14
There is a growing necessity for edge training to adapt to dynamically changing environment. Neuromorphic computing represents a significant pathway for high-efficiency intelligent…
Scalable Dendritic Modeling Advances Expressive and Robust Deep Spiking Neural Networks
Yifan Huang, Wei Fang, Zhengyu Ma +2
Dendritic computation endows biological neurons with rich nonlinear integration and high representational capacity, yet it is largely missing in existing deep spiking neural networ…
Core Placement Optimization of Many-core Brain-Inspired Near-Storage Systems for Spiking Neural Network Training
Xueke Zhu, Wenjie Lin, Yanyu Lin +5
With the increasing application scope of spiking neural networks (SNN), the complexity of SNN models has surged, leading to an exponential growth in demand for AI computility. As t…
Efficiently Training Time-to-First-Spike Spiking Neural Networks from Scratch
Kaiwei Che, Zhengyu Ma, Yifan Huang +5
Spiking Neural Networks (SNNs), with their event-driven and biologically inspired mechanisms, are well-suited for energy-efficient neuromorphic hardware. Neural coding, which is cr…
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…
SVFormer: A Direct Training Spiking Transformer for Efficient Video Action Recognition
Liutao Yu, Liwei Huang, Chenlin Zhou +4
Video action recognition (VAR) plays crucial roles in various domains such as surveillance, healthcare, and industrial automation, making it highly significant for the society. Con…
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…