267 citations · 889 across the 20 of their papers we have counts for
20 papers
Unleashing the Potential of Spiking Neural Networks for Sequential Modeling with Contextual Embedding
Xinyi Chen, Jibin Wu, Huajin Tang +2
The human brain exhibits remarkable abilities in integrating temporally distant sensory inputs for decision-making. However, existing brain-inspired spiking neural networks (SNNs)…
Spiking Neural Network for Ultra-low-latency and High-accurate Object Detection
Jinye Qu, Zeyu Gao, Tielin Zhang +3
Spiking Neural Networks (SNNs) have garnered widespread interest for their energy efficiency and brain-inspired event-driven properties. While recent methods like Spiking-YOLO have…
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…
Temporal Conditioning Spiking Latent Variable Models of the Neural Response to Natural Visual Scenes
Gehua Ma, Runhao Jiang, Rui Yan +1
Developing computational models of neural response is crucial for understanding sensory processing and neural computations. Current state-of-the-art neural network methods use temp…
Exploiting Noise as a Resource for Computation and Learning in Spiking Neural Networks
Gehua Ma, Rui Yan, Huajin Tang
https://cell.com/patterns/fulltext/S2666-3899(23)00200-3 Networks of spiking neurons underpin the extraordinary information-processing capabi…
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…
A Low Latency Adaptive Coding Spiking Framework for Deep Reinforcement Learning
Lang Qin, Rui Yan, Huajin Tang
In recent years, spiking neural networks (SNNs) have been used in reinforcement learning (RL) due to their low power consumption and event-driven features. However, spiking reinfor…
Multi-Level Firing with Spiking DS-ResNet: Enabling Better and Deeper Directly-Trained Spiking Neural Networks
Lang Feng, Qianhui Liu, Huajin Tang +2
Spiking neural networks (SNNs) are bio-inspired neural networks with asynchronous discrete and sparse characteristics, which have increasingly manifested their superiority in low e…
Towards Lossless ANN-SNN Conversion under Ultra-Low Latency with Dual-Phase Optimization
Ziming Wang, Shuang Lian, Yuhao Zhang +3
Spiking neural networks (SNNs) operating with asynchronous discrete events show higher energy efficiency with sparse computation. A popular approach for implementing deep SNNs is A…
Human-Level Control through Directly-Trained Deep Spiking Q-Networks
Guisong Liu, Wenjie Deng, Xiurui Xie +2
As the third-generation neural networks, Spiking Neural Networks (SNNs) have great potential on neuromorphic hardware because of their high energy-efficiency. However, Deep Spiking…
Towards Efficient Processing and Learning with Spikes: New Approaches for Multi-Spike Learning
Qiang Yu, Shenglan Li, Huajin Tang +3
Spikes are the currency in central nervous systems for information transmission and processing. They are also believed to play an essential role in low-power consumption of the bio…
Effective AER Object Classification Using Segmented Probability-Maximization Learning in Spiking Neural Networks
Qianhui Liu, Haibo Ruan, Dong Xing +2
Address event representation (AER) cameras have recently attracted more attention due to the advantages of high temporal resolution and low power consumption, compared with traditi…
Unsupervised AER Object Recognition Based on Multiscale Spatio-Temporal Features and Spiking Neurons
Qianhui Liu, Gang Pan, Haibo Ruan +3
This paper proposes an unsupervised address event representation (AER) object recognition approach. The proposed approach consists of a novel multiscale spatio-temporal feature (Mu…
Robust Environmental Sound Recognition with Sparse Key-point Encoding and Efficient Multi-spike Learning
Qiang Yu, Yanli Yao, Longbiao Wang +3
The capability for environmental sound recognition (ESR) can determine the fitness of individuals in a way to avoid dangers or pursue opportunities when critical sound events occur…
Spiking Deep Residual Network
Yangfan Hu, Huajin Tang, Gang Pan
Spiking neural networks (SNNs) have received significant attention for their biological plausibility. SNNs theoretically have at least the same computational power as traditional a…
Connections Between Nuclear Norm and Frobenius Norm Based Representations
Xi Peng, Canyi Lu, Zhang Yi +1
A lot of works have shown that frobenius-norm based representation (FNR) is competitive to sparse representation and nuclear-norm based representation (NNR) in numerous tasks such…
Fast Low-rank Representation based Spatial Pyramid Matching for Image Classification
Xi Peng, Rui Yan, Bo Zhao +2
Spatial Pyramid Matching (SPM) and its variants have achieved a lot of success in image classification. The main difference among them is their encoding schemes. For example, ScSPM…
A Unified Framework for Representation-based Subspace Clustering of Out-of-sample and Large-scale Data
Xi Peng, Huajin Tang, Lei Zhang +2
Under the framework of spectral clustering, the key of subspace clustering is building a similarity graph which describes the neighborhood relations among data points. Some recent…
Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering
Xi Peng, Zhiding Yu, Huajin Tang +1
Under the framework of graph-based learning, the key to robust subspace clustering and subspace learning is to obtain a good similarity graph that eliminates the effects of errors…