14.5.10.6 Spiking Neural Networks

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.[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

Guo, Y.F.[Yu-Fei], Peng, W.[Weihang], Chen, Y.[Yuanpei], 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.[Yifan], 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

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.[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.F.[Yu-Fei], 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.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.[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 .