Iakymchuk, T.[Taras],
Rosado-Munoz, A.[Alfredo],
Guerrero-Martinez, J.[Juan],
Bataller-Mompean, M.[Manuel],
Frances-Villora, J.[Jose],
Simplified spiking neural network architecture and STDP learning
algorithm applied to image classification,
JIVP(2015), No. 1, 2015, pp. 4.
DOI Link
1503
BibRef
Cao, Y.Q.[Yong-Qiang],
Chen, Y.[Yang],
Khosla, D.[Deepak],
Spiking Deep Convolutional Neural Networks for Energy-Efficient Object
Recognition,
IJCV(113), No. 1, May 2015, pp. 54-66.
Springer DOI
1506
BibRef
Saleh, A.Y.[Abdulrazak Yahya],
Shamsuddin, S.M.[Siti Mariyam],
Hamed, H.N.A.[Haza Nuzly Abdull],
A hybrid differential evolution algorithm for parameter tuning of
evolving spiking neural network,
IJCVR(7), No. 1/2, 2017, pp. 20-34.
DOI Link
1701
BibRef
Falez, P.[Pierre],
Tirilly, P.[Pierre],
Bilasco, I.M.[Ioan Marius],
Devienne, P.[Philippe],
Boulet, P.[Pierre],
Unsupervised visual feature learning with spike-timing-dependent plasticity:
How far are we from traditional feature learning approaches?,
PR(93), 2019, pp. 418-429.
Elsevier DOI
1906
Feature learning, Unsupervised learning,
Spiking neural networks, Spike-timing dependent plasticity,
Image recognition
BibRef
Chakraborty, B.[Biswadeep],
She, X.Y.[Xue-Yuan],
Mukhopadhyay, S.[Saibal],
A Fully Spiking Hybrid Neural Network for Energy-Efficient Object
Detection,
IP(30), 2021, pp. 9014-9029.
IEEE DOI
2112
Biological neural networks, Object detection, Training, Neurons,
Detectors, Standards, Feature extraction, Spiking neural networks,
object detection
BibRef
Zhang, Z.,
Liu, Q.,
Spike-Event-Driven Deep Spiking Neural Network With Temporal Encoding,
SPLetters(28), 2021, pp. 484-488.
IEEE DOI
2103
Neurons, Encoding, Feature extraction, Computational modeling,
Task analysis, Image coding, Biological neural networks,
spiking neural network
BibRef
Chen, J.K.[Jian-Kun],
Qiu, X.L.[Xiao-Lan],
Ding, C.B.[Chi-Biao],
Wu, Y.R.[Yi-Rong],
SAR image classification based on spiking neural network through
spike-time dependent plasticity and gradient descent,
PandRS(188), 2022, pp. 109-124.
Elsevier DOI
2205
Spiking Neural Network (SNN), SAR image classification,
Spike-Time Dependent Plasticity (STDP), Gradient descent
BibRef
Wu, J.[Jibin],
Xu, C.L.[Cheng-Lin],
Han, X.[Xiao],
Zhou, D.Q.[Da-Quan],
Zhang, M.[Malu],
Li, H.Z.[Hai-Zhou],
Tan, K.C.[Kay Chen],
Progressive Tandem Learning for Pattern Recognition With Deep Spiking
Neural Networks,
PAMI(44), No. 11, November 2022, pp. 7824-7840.
IEEE DOI
2210
Neurons, Task analysis, Training,
Biological neural networks, Learning systems, Encoding,
efficient neuromorphic inference
BibRef
Chen, X.Y.[Xin-Yi],
Yang, Q.[Qu],
Wu, J.[Jibin],
Li, H.Z.[Hai-Zhou],
Tan, K.C.[Kay Chen],
A Hybrid Neural Coding Approach for Pattern Recognition With Spiking
Neural Networks,
PAMI(46), No. 5, May 2024, pp. 3064-3078.
IEEE DOI
2404
Encoding, Task analysis, Neurons, Image coding,
Biological information theory, Learning systems,
spiking neural network
BibRef
Rúa, E.A.[Enrique Argones],
van Hamme, T.[Tim],
Preuveneers, D.[Davy],
Joosen, W.[Wouter],
Discriminative training of spiking neural networks organised in
columns for stream-based biometric authentication,
IET-Bio(11), No. 5, 2022, pp. 485-497.
DOI Link
2210
BibRef
Zhan, Q.[Qiugang],
Liu, G.[Guisong],
Xie, X.[Xiurui],
Sun, G.[Guolin],
Tang, H.[Huajin],
Effective Transfer Learning Algorithm in Spiking Neural Networks,
Cyber(52), No. 12, December 2022, pp. 13323-13335.
IEEE DOI
2212
Transfer learning, Deep learning, Neurons, Kernel,
Feature extraction, Artificial neural networks,
transfer learning
BibRef
Zhu, L.[Lin],
Dong, S.W.[Si-Wei],
Li, J.N.[Jia-Ning],
Huang, T.J.[Tie-Jun],
Tian, Y.H.[Yong-Hong],
Ultra-High Temporal Resolution Visual Reconstruction From a
Fovea-Like Spike Camera via Spiking Neuron Model,
PAMI(45), No. 1, January 2023, pp. 1233-1249.
IEEE DOI
2212
Image reconstruction, Cameras, Visualization, Voltage control,
Image sensors, Retina, Neurons, Neuromorphic vision sensor,
bio-inspired vision
BibRef
Guo, Y.F.[Yu-Fei],
Peng, W.H.[Wei-Hang],
Chen, Y.P.[Yuan-Pei],
Zhang, L.W.[Li-Wen],
Liu, X.[Xiaode],
Huang, X.[Xuhui],
Ma, Z.[Zhe],
Joint A-SNN: Joint training of artificial and spiking neural networks
via self-Distillation and weight factorization,
PR(142), 2023, pp. 109639.
Elsevier DOI
2307
Spiking neural networks, Artificial neural networks,
Knowledge distillation, Weight factorization
BibRef
Yao, M.[Man],
Zhao, G.S.[Guang-She],
Zhang, H.Y.[Heng-Yu],
Hu, Y.F.[Yi-Fan],
Deng, L.[Lei],
Tian, Y.H.[Yong-Hong],
Xu, B.[Bo],
Li, G.Q.[Guo-Qi],
Attention Spiking Neural Networks,
PAMI(45), No. 8, August 2023, pp. 9393-9410.
IEEE DOI
2307
Training, Energy efficiency, Visualization, Task analysis,
Membrane potentials, Biological neural networks, Degradation,
spiking neural network
BibRef
Yan, Z.L.[Zhang-Lu],
Zhou, J.[Jun],
Wong, W.F.[Weng-Fai],
CQ+ Training: Minimizing Accuracy Loss in Conversion From
Convolutional Neural Networks to Spiking Neural Networks,
PAMI(45), No. 10, October 2023, pp. 11600-11611.
IEEE DOI
2310
BibRef
Eshraghian, J.K.[Jason K.],
Ward, M.[Max],
Neftci, E.O.[Emre O.],
Wang, X.X.[Xin-Xin],
Lenz, G.[Gregor],
Dwivedi, G.[Girish],
Bennamoun, M.[Mohammed],
Jeong, D.S.[Doo Seok],
Lu, W.D.[Wei D.],
Training Spiking Neural Networks Using Lessons From Deep Learning,
PIEEE(111), No. 9, September 2023, pp. 1016-1054.
IEEE DOI Code:
HTML Version.
2310
BibRef
Tang, J.X.[Jian-Xiong],
Lai, J.H.[Jian-Huang],
Xie, X.H.[Xiao-Hua],
Yang, L.X.[Ling-Xiao],
Zheng, W.S.[Wei-Shi],
AC2AS: Activation Consistency Coupled ANN-SNN framework for fast and
memory-efficient SNN training,
PR(144), 2023, pp. 109826.
Elsevier DOI
2310
Spiking neural networks, Deep learning, Supervised learning,
Image classification
BibRef
Wang, S.[Shuo],
Peng, Y.Y.X.[Yuan-Yan-Xi],
Wang, L.[Lei],
Li, T.[Teng],
Boundary-Aware Deformable Spiking Neural Network for Hyperspectral
Image Classification,
RS(15), No. 20, 2023, pp. 5020.
DOI Link
2310
BibRef
Hu, Y.F.[Yang-Fan],
Zheng, Q.[Qian],
Jiang, X.D.[Xu-Dong],
Pan, G.[Gang],
Fast-SNN: Fast Spiking Neural Network by Converting Quantized ANN,
PAMI(45), No. 12, December 2023, pp. 14546-14562.
IEEE DOI Code:
WWW Link.
2311
BibRef
Duan, P.Q.[Pei-Qi],
Ma, Y.[Yi],
Zhou, X.Y.[Xin-Yu],
Shi, X.Y.[Xin-Yu],
Wang, Z.H.W.[Zi-Hao W.],
Huang, T.J.[Tie-Jun],
Shi, B.X.[Bo-Xin],
NeuroZoom: Denoising and Super Resolving Neuromorphic Events and
Spikes,
PAMI(45), No. 12, December 2023, pp. 15219-15232.
IEEE DOI
2311
BibRef
Jeyasothy, A.[Abeegithan],
Suresh, S.[Sundaram],
Ramasamy, S.[Savitha],
Sundararajan, N.[Narasimhan],
Development of a Novel Transformation of Spiking Neural Classifier to
an Interpretable Classifier,
Cyber(54), No. 1, January 2024, pp. 3-12.
IEEE DOI
2312
BibRef
Yang, F.[Fan],
Su, L.[Li],
Zhao, J.X.[Jin-Xiu],
Chen, X.[Xuena],
Wang, X.Y.[Xiang-Yu],
Jiang, N.[Na],
Hu, Q.[Quan],
SA-FlowNet: Event-based self-attention optical flow estimation with
spiking-analogue neural networks,
IET-CV(17), No. 8, 2023, pp. 925-935.
DOI Link
2312
feature extraction, motion estimation, optical tracking
BibRef
Yang, S.M.[Shuang-Ming],
Wang, H.W.[Hao-Wen],
Pang, Y.W.[Yan-Wei],
Jin, Y.C.[Yao-Chu],
Linares-Barranco, B.[Bernabé],
Integrating Visual Perception With Decision Making in Neuromorphic
Fault-Tolerant Quadruplet-Spike Learning Framework,
SMCS(54), No. 3, March 2024, pp. 1502-1514.
IEEE DOI
2402
Neurons, Fault tolerant systems, Fault tolerance, Decision making,
Visual perception, Neuromorphic engineering, Biology,
spiking neural network (SNN)
BibRef
Zhao, R.[Rui],
Xiong, R.Q.[Rui-Qin],
Zhang, J.[Jian],
Yu, Z.F.[Zhao-Fei],
Zhu, S.Y.[Shu-Yuan],
Ma, L.[Lei],
Huang, T.J.[Tie-Jun],
Spike Camera Image Reconstruction Using Deep Spiking Neural Networks,
CirSysVideo(34), No. 6, June 2024, pp. 5207-5212.
IEEE DOI
2406
Cameras, Image reconstruction, Neurons, Feature extraction, Convolution,
Training, Streaming media, Spike camera, spiking neural networks
BibRef
Zhao, J.W.[Jun-Wei],
Zhang, S.L.[Shi-Liang],
Yu, Z.F.[Zhao-Fei],
Huang, T.J.[Tie-Jun],
SpiReco: Fast and Efficient Recognition of High-Speed Moving Objects
With Spike Camera,
CirSysVideo(34), No. 7, July 2024, pp. 5856-5867.
IEEE DOI Code:
WWW Link.
2407
Cameras, Object recognition, Task analysis, Neuromorphics,
Monitoring, Recording, Neural networks, Neuromorphic vision,
spiking neural networks
BibRef
Li, Y.H.[Yu-Hang],
Deng, S.K.[Shi-Kuang],
Dong, X.[Xin],
Gu, S.[Shi],
Error-Aware Conversion from ANN to SNN via Post-training Parameter
Calibration,
IJCV(132), No. 1, January 2024, pp. 3586-3609.
Springer DOI
2409
BibRef
Yao, Z.W.[Zhi-Wei],
Gao, S.B.[Shao-Bing],
Li, W.J.[Wen-Juan],
SNN using color-opponent and attention mechanisms for object
recognition,
PR(158), 2025, pp. 111070.
Elsevier DOI
2411
spiking neural network.
Color-opponency, Attention mechanism, STDP, SNN, Unsupervised learning
BibRef
Han, B.[Bing],
Zhao, F.F.[Fei-Fei],
Zeng, Y.[Yi],
Shen, G.B.[Guo-Bin],
Developmental Plasticity-Inspired Adaptive Pruning for Deep Spiking
and Artificial Neural Networks,
PAMI(47), No. 1, January 2025, pp. 240-251.
IEEE DOI
2412
Neurons, Synapses, Biology, Adaptive systems, Current measurement,
Particle measurements, Heuristic algorithms,
integrated development mechanisms
BibRef
Zhao, D.C.[Dong-Cheng],
Shen, G.B.[Guo-Bin],
Dong, Y.T.[Yi-Ting],
Li, Y.[Yang],
Zeng, Y.[Yi],
Improving stability and performance of spiking neural networks
through enhancing temporal consistency,
PR(159), 2025, pp. 111094.
Elsevier DOI
2412
Spiking neural networks, Enhancing temporal consistency, Low latency
BibRef
Zhang, J.Q.[Ji-Qing],
Zhang, M.[Malu],
Wang, Y.[Yuanchen],
Liu, Q.H.[Qian-Hui],
Yin, B.C.[Bao-Cai],
Li, H.Z.[Hai-Zhou],
Yang, X.[Xin],
Spiking Neural Networks With Adaptive Membrane Time Constant for
Event-Based Tracking,
IP(34), 2025, pp. 1009-1021.
IEEE DOI
2502
Neurons, Event detection, Cameras, Feature extraction, Biomembranes,
Visualization, Spiking neural networks, Object tracking, Streams,
visual object tracking
BibRef
Liu, Z.X.[Zhao-Xin],
Wu, J.J.[Jin-Jian],
Shi, G.M.[Guang-Ming],
Yang, W.[Wen],
Ma, J.[Jupo],
An event-based motion scene feature extraction framework,
PR(161), 2025, pp. 111320.
Elsevier DOI
2502
Motion object feature, Spiking neural networks,
Cross-modal feature fusion, Motion deblurring
BibRef
Yang, Z.[Zhile],
Guo, S.Q.[Shang-Qi],
Fang, Y.[Ying],
Yu, Z.F.[Zhao-Fei],
Liu, J.K.[Jian K.],
Spiking Variational Policy Gradient for Brain Inspired Reinforcement
Learning,
PAMI(47), No. 3, March 2025, pp. 1975-1990.
IEEE DOI
2502
Neurons, Firing, Synapses, Integrated circuit modeling, Robustness,
Reinforcement learning, Navigation, Trajectory, Training,
variational policy gradient
BibRef
Ribeiro, B.[Bernardete],
Antunes, F.[Francisco],
Perdigão, D.[Dylan],
Silva, C.[Catarina],
Convolutional Spiking Neural Networks targeting learning and
inference in highly imbalanced datasets,
PRL(189), 2025, pp. 241-247.
Elsevier DOI
2503
Spiking Neural Networks, Convolutional neural networks,
Learning, Hyperparameter optimization
BibRef
Yao, M.[Man],
Qiu, X.R.[Xue-Rui],
Hu, T.X.[Tian-Xiang],
Hu, J.[Jiakui],
Chou, Y.H.[Yu-Hong],
Tian, K.Y.[Ke-Yu],
Liao, J.X.[Jian-Xing],
Leng, L.W.[Luzi-Wei],
Xu, B.[Bo],
Li, G.Q.[Guo-Qi],
Scaling Spike-Driven Transformer With Efficient Spike Firing
Approximation Training,
PAMI(47), No. 4, April 2025, pp. 2973-2990.
IEEE DOI
2503
Training, Neurons, Firing, Transformers, Membrane potentials,
Visualization, Convolution, Accuracy,
efficient architecture and training
BibRef
Luo, X.H.[Xin-Hao],
Yao, M.[Man],
Chou, Y.H.[Yu-Hong],
Xu, B.[Bo],
Li, G.Q.[Guo-Qi],
Integer-valued Training and Spike-driven Inference Spiking Neural
Network for High-performance and Energy-efficient Object Detection,
ECCV24(XXXII: 253-272).
Springer DOI
2412
BibRef
Chen, G.J.[Guo-Jun],
Wang, G.[Guoen],
TSTKD: Triple-spike train kernel-driven supervised learning algorithm,
PR(164), 2025, pp. 111525.
Elsevier DOI
2504
Spiking neuron, Spike train kernel, Spike pattern learning,
Supervised learning algorithm, Triple spikes
BibRef
Wu, X.F.[Xiao-Feng],
Bojkovic, V.[Velibor],
Gu, B.[Bin],
Suo, K.[Kun],
Zou, K.[Kai],
FTBC: Forward Temporal Bias Correction for Optimizing ANN-SNN
Conversion,
ECCV24(LXIX: 155-173).
Springer DOI
2412
BibRef
Yao, Y.[Yanmeng],
Zhao, X.H.[Xiao-Han],
Gu, B.[Bin],
Exploring Vulnerabilities in Spiking Neural Networks: Direct
Adversarial Attacks on Raw Event Data,
ECCV24(LXXII: 412-428).
Springer DOI
2412
BibRef
Kachole, S.[Sanket],
Sajwani, H.[Hussain],
Naeini, F.B.[Fariborz Baghaei],
Makris, D.[Dimitrios],
Zweiri, Y.[Yahya],
Asynchronous Bioplausible Neuron for Spiking Neural Networks for
Event-based Vision,
ECCV24(LXIV: 399-415).
Springer DOI
2412
BibRef
Wang, Z.M.[Zi-Ming],
Wang, Z.[Ziling],
Li, H.[Huaning],
Qin, L.[Lang],
Jiang, R.[Runhao],
Ma, D.[De],
Tang, H.[Huajin],
EAS-SNN: End-to-end Adaptive Sampling and Representation for
Event-based Detection with Recurrent Spiking Neural Networks,
ECCV24(LX: 310-328).
Springer DOI
2412
BibRef
Cao, Z.G.[Zhi-Gao],
Li, M.[Meng],
Wang, X.S.[Xia-Shuang],
Wang, H.Y.[Hao-Yu],
Wang, F.[Fan],
Li, Y.J.[You-Jun],
Huang, Z.G.[Zi-Gang],
Efficient Training of Spiking Neural Networks with Multi-parallel
Implicit Stream Architecture,
ECCV24(XXXIV: 422-438).
Springer DOI
2412
BibRef
Fan, Y.M.[Yi-Meng],
Zhang, W.[Wei],
Liu, C.S.[Chang-Song],
Li, M.Y.[Ming-Yang],
Lu, W.R.[Wen-Rui],
SFOD: Spiking Fusion Object Detector,
CVPR24(17191-17200)
IEEE DOI Code:
WWW Link.
2410
Codes, Power demand, Object detection, Detectors,
Spiking neural networks, Propulsion, Cameras, Object Detection
BibRef
Shen, G.B.[Guo-Bin],
Zhao, D.C.[Dong-Cheng],
Li, T.L.[Teng-Long],
Li, J.D.[Jin-Dong],
Zeng, Y.[Yi],
Are Conventional SNNs Really Efficient? A Perspective from Network
Quantization,
CVPR24(27528-27537)
IEEE DOI
2410
Quantization (signal), Neuroscience, Systematics,
Computational modeling, Buildings, Spiking neural networks, Energy efficiency
BibRef
Lin, Y.[Yang],
Charbon, E.[Edoardo],
Spiking Neural Networks for Active Time-Resolved SPAD Imaging,
WACV24(8132-8141)
IEEE DOI
2404
Training, Imaging, Virtual reality, Throughput, Real-time systems,
Sensors, Smart phones, Applications,
Biomedical / healthcare / medicine
BibRef
Bulzomi, H.[Hugo],
Gruel, A.[Amélie],
Martinet, J.[Jean],
Fujita, T.[Takeshi],
Nakano, Y.[Yuta],
Bendahan, R.[Rémy],
Object Detection for Embedded Systems Using Tiny Spiking Neural
Networks: Filtering Noise Through Visual Attention,
MVA23(1-5)
DOI Link
2403
Visualization, Embedded systems, Costs, Neuromorphics,
Object detection, Cameras
BibRef
Su, Q.Y.[Qiao-Yi],
Chou, Y.H.[Yu-Hong],
Hu, Y.F.[Yi-Fan],
Li, J.N.[Jia-Ning],
Mei, S.J.[Shi-Jie],
Zhang, Z.Y.[Zi-Yang],
Li, G.Q.[Guo-Qi],
Deep Directly-Trained Spiking Neural Networks for Object Detection,
ICCV23(6532-6542)
IEEE DOI Code:
WWW Link.
2401
BibRef
Lan, Y.X.[Yu-Xiang],
Zhang, Y.[Yachao],
Ma, X.[Xu],
Qu, Y.[Yanyun],
Fu, Y.[Yun],
Efficient Converted Spiking Neural Network for 3D and 2D
Classification,
ICCV23(9177-9186)
IEEE DOI
2401
BibRef
Wei, W.J.[Wen-Jie],
Zhang, M.[Malu],
Qu, H.[Hong],
Belatreche, A.[Ammar],
Zhang, J.[Jian],
Chen, H.[Hong],
Temporal-Coded Spiking Neural Networks with Dynamic Firing Threshold:
Learning with Event-Driven Backpropagation,
ICCV23(10518-10528)
IEEE DOI
2401
BibRef
Li, C.[Chen],
Jones, E.G.[Edward G],
Furber, S.[Steve],
Unleashing the Potential of Spiking Neural Networks with Dynamic
Confidence,
ICCV23(13304-13314)
IEEE DOI
2401
BibRef
Yao, M.[Man],
Hu, J.[Jiakui],
Zhao, G.[Guangshe],
Wang, Y.[Yaoyuan],
Zhang, Z.Y.[Zi-Yang],
Xu, B.[Bo],
Li, G.Q.[Guo-Qi],
Inherent Redundancy in Spiking Neural Networks,
ICCV23(16878-16888)
IEEE DOI Code:
WWW Link.
2401
BibRef
Meng, Q.Y.[Qing-Yan],
Xiao, M.Q.[Ming-Qing],
Yan, S.[Shen],
Wang, Y.[Yisen],
Lin, Z.C.[Zhou-Chen],
Luo, Z.Q.[Zhi-Quan],
Towards Memory- and Time-Efficient Backpropagation for Training
Spiking Neural Networks,
ICCV23(6143-6153)
IEEE DOI Code:
WWW Link.
2401
BibRef
Wang, J.T.[Jing-Tao],
Song, Z.J.[Zeng-Jie],
Wang, Y.X.[Yu-Xi],
Xiao, J.[Jun],
Yang, Y.[Yuran],
Mei, S.Q.[Shu-Qi],
Zhang, Z.X.[Zhao-Xiang],
SSF: Accelerating Training of Spiking Neural Networks with Stabilized
Spiking Flow,
ICCV23(5959-5968)
IEEE DOI
2401
BibRef
Guo, Y.F.[Yu-Fei],
Liu, X.[Xiaode],
Chen, Y.P.[Yuan-Pei],
Zhang, L.W.[Li-Wen],
Peng, W.H.[Wei-Hang],
Zhang, Y.H.[Yu-Han],
Huang, X.[Xuhui],
Ma, Z.[Zhe],
RMP-Loss: Regularizing Membrane Potential Distribution for Spiking
Neural Networks,
ICCV23(17345-17355)
IEEE DOI
2401
BibRef
And: A1, A6, A3, A5, A2, A4, A7, A8:
Membrane Potential Batch Normalization for Spiking Neural Networks,
ICCV23(19363-19373)
IEEE DOI
2401
BibRef
Kang, P.[Peng],
Banerjee, S.[Srutarshi],
Chopp, H.[Henry],
Katsaggelos, A.K.[Aggelos K.],
Cossairt, O.[Oliver],
Spiking GLOM: Bio-Inspired Architecture for Next-Generation Object
Recognition,
ICIP23(950-954)
IEEE DOI
2312
BibRef
Bu, T.[Tong],
Ding, J.H.[Jian-Hao],
Hao, Z.C.[Ze-Cheng],
Yu, Z.F.[Zhao-Fei],
Rate Gradient Approximation Attack Threats Deep Spiking Neural
Networks,
CVPR23(7896-7906)
IEEE DOI
2309
BibRef
Auge, D.[Daniel],
Hille, J.[Julian],
Mueller, E.[Etienne],
Knoll, A.[Alois],
Hand Gesture Recognition in Range-Doppler Images Using Binary
Activated Spiking Neural Networks,
FG21(01-07)
IEEE DOI
2303
Privacy, Neuromorphics, Neurons, Signal processing algorithms,
Gesture recognition, Radar, Radar imaging
BibRef
Dutson, M.[Matthew],
Li, Y.[Yin],
Gupta, M.[Mohit],
Spike-Based Anytime Perception,
WACV23(5283-5293)
IEEE DOI
2302
Measurement, Training, Power demand, Neuromorphics, Machine vision,
Neural networks, Algorithms: Machine learning architectures,
Embedded sensing/real-time techniques
BibRef
Gruel, A.[Amélie],
Martinet, J.[Jean],
Linares-Barranco, B.[Bernabé],
Serrano-Gotarredona, T.[Teresa],
Performance comparison of DVS data spatial downscaling methods using
Spiking Neural Networks,
WACV23(6483-6491)
IEEE DOI
2302
Embedded systems, Neuromorphics, Neural networks,
Robot vision systems, Vision sensors, Real-time systems,
Embedded sensing/real-time techniques
BibRef
Li, Y.H.[Yu-Hang],
Kim, Y.[Youngeun],
Park, H.[Hyoungseob],
Geller, T.[Tamar],
Panda, P.[Priyadarshini],
Neuromorphic Data Augmentation for Training Spiking Neural Networks,
ECCV22(VII:631-649).
Springer DOI
2211
BibRef
Kim, Y.[Youngeun],
Li, Y.H.[Yu-Hang],
Park, H.[Hyoungseob],
Venkatesha, Y.[Yeshwanth],
Panda, P.[Priyadarshini],
Neural Architecture Search for Spiking Neural Networks,
ECCV22(XXIV:36-56).
Springer DOI
2211
BibRef
Stanojevic, A.[Ana],
Eleftheriou, E.[Evangelos],
Cherubini, G.[Giovanni],
Wozniak, S.[Stanislaw],
Pantazi, A.[Angeliki],
Gerstner, W.[Wulfram],
Approximating Relu Networks by Single-Spike Computation,
ICIP22(1901-1905)
IEEE DOI
2211
Training, Adaptation models, Visualization,
Biological system modeling, Neurons, Hardware, efficient classification
BibRef
Cohen-Duwek, H.[Hadar],
Tsur, E.E.[Elishai Ezra],
Biologically Plausible Illusionary Contrast Perception with Spiking
Neural Networks,
ICIP22(1586-1590)
IEEE DOI
2211
Surface reconstruction, Neuromorphics, Computational modeling,
Biological system modeling, Neurons, Iterative methods,
visual perception
BibRef
Grimaldi, A.[Antoine],
Perrinet, L.U.[Laurent U],
Learning hetero-synaptic delays for motion detection in a single
layer of spiking neurons,
ICIP22(3591-3595)
IEEE DOI
2211
Neuromorphics, Neurons, Cameras, Motion detection, Delays,
Synchronization, Biological neural networks, time code,
logistic regression
BibRef
Kim, Y.[Youngeun],
Li, Y.H.[Yu-Hang],
Park, H.[Hyoungseob],
Venkatesha, Y.[Yeshwanth],
Yin, R.[Ruokai],
Panda, P.[Priyadarshini],
Exploring Lottery Ticket Hypothesis in Spiking Neural Networks,
ECCV22(XII:102-120).
Springer DOI
2211
BibRef
Guo, Y.F.[Yu-Fei],
Zhang, L.W.[Li-Wen],
Chen, Y.P.[Yuan-Pei],
Tong, X.Y.[Xin-Yi],
Liu, X.[Xiaode],
Wang, Y.L.[Ying-Lei],
Huang, X.[Xuhui],
Ma, Z.[Zhe],
Real Spike: Learning Real-Valued Spikes for Spiking Neural Networks,
ECCV22(XII:52-68).
Springer DOI
2211
BibRef
Chowdhury, S.S.[Sayeed Shafayet],
Rathi, N.[Nitin],
Roy, K.[Kaushik],
Towards Ultra Low Latency Spiking Neural Networks for Vision and
Sequential Tasks Using Temporal Pruning,
ECCV22(XI:709-726).
Springer DOI
2211
BibRef
Guo, Y.F.[Yu-Fei],
Chen, Y.P.[Yuan-Pei],
Zhang, L.W.[Li-Wen],
Wang, Y.L.[Ying-Lei],
Liu, X.[Xiaode],
Tong, X.Y.[Xin-Yi],
Ou, Y.Y.[Yuan-Yuan],
Huang, X.[Xuhui],
Ma, Z.[Zhe],
Reducing Information Loss for Spiking Neural Networks,
ECCV22(XI:36-52).
Springer DOI
2211
BibRef
Zhou, S.[Shibo],
Li, X.H.[Xiao-Hua],
Spiking Neural Networks with Single-Spike Temporal-Coded Neurons for
Network Intrusion Detection,
ICPR21(8148-8155)
IEEE DOI
2105
Training, Neurons, Network intrusion detection, Machine learning,
Energy efficiency, Biological neural networks
BibRef
Li, W.S.[Wen-Shuo],
Chen, H.T.[Han-Ting],
Guo, J.Y.[Jian-Yuan],
Zhang, Z.Y.[Zi-Yang],
Wang, Y.H.[Yun-He],
Brain-inspired Multilayer Perceptron with Spiking Neurons,
CVPR22(773-783)
IEEE DOI
2210
Computational modeling, Neurons, Multilayer perceptrons,
Feature extraction, Brain modeling, Transformers,
Machine learning
BibRef
Zhu, L.[Lin],
Wang, X.[Xiao],
Chang, Y.[Yi],
Li, J.N.[Jia-Ning],
Huang, T.J.[Tie-Jun],
Tian, Y.H.[Yong-Hong],
Event-based Video Reconstruction via Potential-assisted Spiking
Neural Network,
CVPR22(3584-3594)
IEEE DOI
2210
Adaptation models, Computational modeling, Neurons,
Membrane potentials,
Computational photography
BibRef
Zhang, J.Q.[Ji-Qing],
Dong, B.[Bo],
Zhang, H.W.[Hai-Wei],
Ding, J.C.[Jian-Chuan],
Heide, F.[Felix],
Yin, B.C.[Bao-Cai],
Yang, X.[Xin],
Spiking Transformers for Event-based Single Object Tracking,
CVPR22(8791-8800)
IEEE DOI
2210
Fuses, Heuristic algorithms, Neural networks, Dynamics,
Feature extraction, Transformers, Robustness, Motion and tracking
BibRef
Meng, Q.Y.[Qing-Yan],
Xiao, M.Q.[Ming-Qing],
Yan, S.[Shen],
Wang, Y.S.[Yi-Sen],
Lin, Z.C.[Zhou-Chen],
Luo, Z.Q.[Zhi-Quan],
Training High-Performance Low-Latency Spiking Neural Networks by
Differentiation on Spike Representation,
CVPR22(12434-12443)
IEEE DOI
2210
Training, Deep learning, Neuromorphics, Firing,
Computational modeling, Hardware, Energy efficiency,
Deep learning architectures and techniques
BibRef
Guo, Y.F.[Yu-Fei],
Tong, X.Y.[Xin-Yi],
Chen, Y.P.[Yuan-Pei],
Zhang, L.W.[Li-Wen],
Liu, X.[Xiaode],
Ma, Z.[Zhe],
Huang, X.[Xuhui],
RecDis-SNN: Rectifying Membrane Potential Distribution for Directly
Training Spiking Neural Networks,
CVPR22(326-335)
IEEE DOI
2210
Training, Quantization (signal), Neuromorphics, Firing, Neurons,
Membrane potentials, Energy efficiency, Machine learning,
retrieval
BibRef
Jang, H.[Hyeryung],
Skatchkovsky, N.[Nicolas],
Simeone, O.[Osvaldo],
VOWEL: A Local Online Learning Rule for Recurrent Networks of
Probabilistic Spiking Winner- Take-All Circuits,
ICPR21(4597-4604)
IEEE DOI
2105
Training, Neuromorphics, Neurons, Probabilistic logic, Hardware,
Timing,
Neuromorphic Computing
BibRef
Barbier, T.[Thomas],
Teulière, C.[Céline],
Triesch, J.[Jochen],
Spike timing-based unsupervised learning of orientation, disparity,
and motion representations in a spiking neural network*,
EventVision21(1377-1386)
IEEE DOI
2109
Visualization, Neuromorphics, Neurons, Detectors,
Vision sensors, Robot sensing systems
BibRef
Fang, W.[Wei],
Yu, Z.F.[Zhao-Fei],
Chen, Y.Q.[Yan-Qi],
Masquelier, T.[Timothée],
Huang, T.J.[Tie-Jun],
Tian, Y.H.[Yong-Hong],
Incorporating Learnable Membrane Time Constant to Enhance Learning of
Spiking Neural Networks,
ICCV21(2641-2651)
IEEE DOI
2203
Training, Costs, Power demand, Neuromorphics, Neurons, Manuals,
Biomembranes, Computational photography, Recognition and classification
BibRef
Garg, I.[Isha],
Chowdhury, S.S.[Sayeed Shafayet],
Roy, K.[Kaushik],
DCT-SNN: Using DCT to Distribute Spatial Information over Time for
Low-Latency Spiking Neural Networks,
ICCV21(4651-4660)
IEEE DOI
2203
Deep learning, Time-frequency analysis, Neurons, Transforms,
Encoding, Computational efficiency, Discrete cosine transforms,
Vision applications and systems
BibRef
Kundu, S.[Souvik],
Pedram, M.[Massoud],
Beerel, P.A.[Peter A.],
HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep
Spiking Neural Networks by Training with Crafted Input Noise,
ICCV21(5189-5198)
IEEE DOI
2203
Training, Costs, Computational modeling, Robustness,
Energy efficiency, Reproducibility of results, Emergency Reviewer
BibRef
Kundu, S.[Souvik],
Datta, G.[Gourav],
Pedram, M.[Massoud],
Beerel, P.A.[Peter A.],
Spike-Thrift: Towards Energy-Efficient Deep Spiking Neural Networks
by Limiting Spiking Activity via Attention-Guided Compression,
WACV21(3952-3961)
IEEE DOI
2106
Training, Machine learning algorithms, Limiting, Firing,
Computational modeling, Artificial neural networks, Machine learning
BibRef
Han, B.[Bing],
Roy, K.[Kaushik],
Deep Spiking Neural Network: Energy Efficiency Through Time Based
Coding,
ECCV20(X:388-404).
Springer DOI
2011
BibRef
Sharmin, S.[Saima],
Rathi, N.[Nitin],
Panda, P.[Priyadarshini],
Roy, K.[Kaushik],
Inherent Adversarial Robustness of Deep Spiking Neural Networks:
Effects of Discrete Input Encoding and Non-linear Activations,
ECCV20(XXIX: 399-414).
Springer DOI
2010
BibRef
Han, B.,
Srinivasan, G.,
Roy, K.,
RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper
High-Accuracy and Low-Latency Spiking Neural Network,
CVPR20(13555-13564)
IEEE DOI
2008
Training, Biological neural networks, Image recognition,
Task analysis, Backpropagation
BibRef
Valadez-Godínez, S.[Sergio],
González, J.[Javier],
Sossa, H.[Humberto],
Efficient Pattern Recognition Using the Frequency Response of a Spiking
Neuron,
MCPR17(53-62).
Springer DOI
1706
BibRef
Xiang, Y.[Yande],
Meng, J.Y.[Jian-Yi],
Ma, D.[De],
A load balanced mapping for spiking neural network,
ICIVC17(899-903)
IEEE DOI
1708
Acceleration, Biology, Handwriting recognition, Neural networks,
Quality of service, Sociology, Statistics, NoC, execution time,
neural mapping, spiking neural network (SNN).
BibRef
Espinal, A.[Andrés],
Carpio, M.[Martín],
Ornelas, M.[Manuel],
Puga, H.[Héctor],
Melín, P.[Patricia],
Sotelo-Figueroa, M.[Marco],
Developing Architectures of Spiking Neural Networks by Using
Grammatical Evolution Based on Evolutionary Strategy,
MCPR14(71-80).
Springer DOI
1407
BibRef
Wysoski, S.G.[Simei Gomes],
Benuskova, L.[Lubica],
Kasabov, N.[Nikola],
Adaptive Learning Procedure for a Network of Spiking Neurons and Visual
Pattern Recognition,
ACIVS06(1133-1142).
Springer DOI
0609
BibRef
Thorpe, S.[Simon],
Ultra-Rapid Scene Categorization with a Wave of Spikes,
BMCV02(1 ff.).
Springer DOI
0303
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Convolutional Neural Networks for Image Descriptions, Classification .