Iakymchuk, T.[Taras],
Rosado-Munoz, A.[Alfredo],
Guerrero-Martinez, J.[Juan],
Bataller-Mompean, M.[Manuel],
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Spiking Deep Convolutional Neural Networks for Energy-Efficient Object
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IJCV(113), No. 1, May 2015, pp. 54-66.
Springer DOI
1506
BibRef
Saleh, A.Y.[Abdulrazak Yahya],
Shamsuddin, S.M.[Siti Mariyam],
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IJCVR(7), No. 1/2, 2017, pp. 20-34.
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BibRef
Falez, P.[Pierre],
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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.[Xueyuan],
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, Pattern recognition,
Biological neural networks, Learning systems, Encoding,
efficient neuromorphic inference
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
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.[Yufei],
Zhang, L.W.[Li-Wen],
Chen, Y.[Yuanpei],
Tong, X.[Xinyi],
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.[Yufei],
Chen, Y.P.[Yuan-Pei],
Zhang, L.W.[Li-Wen],
Wang, Y.L.[Ying-Lei],
Liu, X.[Xiaode],
Tong, X.[Xinyi],
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, Pattern recognition, Biological neural networks
BibRef
Li, W.S.[Wen-Shuo],
Chen, H.T.[Han-Ting],
Guo, J.Y.[Jian-Yuan],
Zhang, Z.[Ziyang],
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.[Xinyi],
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, Pattern recognition,
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 .