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Spiking neural network (SNN) has attracted much attention due to their powerful spatio-temporal information representation ability.
Spiking-yolo: Spiking neural network for real-time object detection
Kim, S.; Park, S.; Na, B.; and Yoon, S. 2019 · 1903
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Spike-train level backpropagation for training deep recurrent spiking neural networks
Zhang, W.; and Li, P. 2019 · 1908
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Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type
Bi, G.-q.; and Poo, M.-m. 1998 · 1998
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Capsules with inverted dot-product attention routing
Tsai, Y.-H. H.; Srivastava, N.; Goh, H.; and Salakhutdinov, R. 2020 · 2002
Earlier work this paper cites.
Temporal spike sequence learning via backpropagation for deep spiking neural networks
Zhang, W.; and Li, P. 2020 · 2002
Earlier work this paper cites.
Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks
Zhang, M.; Wang, J.; Amornpaisannon, B.; Zhang, Z.; Miriyala, V.; Belatreche, A.; Qu, H.; Wu, J.; Chua, Y.; Carlson, T. E.; et al. 2020 · 2003
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The organization of behavior: A neuropsychological theory
Hebb, D. O. 2005 · 2005
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Towards Understanding the Effect of Leak in Spiking Neural Networks
Chowdhury, S. S.; Lee, C.; and Roy, K. 2020 · 2006
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Spike-based feature extraction for noise robust speech recognition using phase synchrony coding
Uysal, I.; Sathyendra, H.; and Harris, J. G. 2007 · 2007
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Adam: A method for stochastic optimization
Kingma, D. P.; and Ba, J. 2014 · 2014
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Convolutional features for correlation filter based visual tracking
Danelljan, M.; Hager, G.; Shahbaz Khan, F.; and Felsberg, M. 2015 · 2015
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Unsupervised learning of digit recognition using spike-timing-dependent plasticity
Diehl, P. U.; and Cook, M. 2015 · 2015
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Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
Diehl, P. U.; Neil, D.; Binas, J.; Cook, M.; Liu, S.-C.; and Pfeiffer, M. 2015 · 2015
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L.-C.; Papandreou, G.; Kokkinos, I.; Murphy, K.; and Yuille, A. L. 2017 · 2017
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Automatic differentiation in pytorch
Paszke, A.; Gross, S.; Chintala, S.; Chanan, G.; Yang, E.; DeVito, Z.; Lin, Z.; Desmaison, A.; Antiga, L.; and Lerer, A. 2017 · 2017
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Conversion of continuous-valued deep networks to efficient event-driven networks for image classification
Rueckauer, B.; Lungu, I.-A.; Hu, Y.; Pfeiffer, M.; and Liu, S.-C. 2017 · 2017
Cited alongside, same era.
Dynamic routing between capsules
Sabour, S.; Frosst, N.; and Hinton, G. E. 2017 · 2017
Cited alongside, same era.
Generalized capsule networks with trainable routing procedure
Chen, Z.; and Crandall, D. 2018 · 2018
Cited alongside, same era.
Matrix capsules with EM routing
Hinton, G. E.; Sabour, S.; and Frosst, N. 2018 · 2018
Cited alongside, same era.
Hybrid macro/micro level backpropagation for training deep spiking neural networks
Jin, Y.; Zhang, W.; and Li, P. 2018 · 2018
Cited alongside, same era.
Towards artificial general intelligence with hybrid Tianjic chip architecture
Pei, J.; Deng, L.; Song, S.; Zhao, M.; Zhang, Y.; Wu, S.; Wang, G.; Zou, Z.; Wu, Z.; He, W.; et al. 2019 · 2019
Later among the works it cites.
Towards spike-based machine intelligence with neuromorphic computing
Roy, K.; Jaiswal, A.; and Panda, P. 2019 · 2019
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Approximating back-propagation for a biologically plausible local learning rule in spiking neural networks
Shrestha, A.; Fang, H.; Wu, Q.; and Qiu, Q. 2019 · 2019
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A Spiking Network for Inference of Relations Trained with Neuromorphic Backpropagation
Thiele, J. C.; Bichler, O.; Dupret, A.; Solinas, S.; and Indiveri, G. 2019 · 2019
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Direct training for spiking neural networks: Faster, larger, better
Wu, Y.; Deng, L.; Li, G.; Zhu, J.; Xie, Y.; and Shi, L. 2019 · 2019
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Lenssen, J. E.; Fey, M.; and Libuschewski, P. 2018 · 2018
Cited alongside, same era.
Neural network encapsulation
Li, H.; Guo, X.; Ouyang, B. D.; and Wang, X. 2018 · 2018
Cited alongside, same era.
Training spiking convnets by stdp and gradient descent
Tavanaei, A.; Kirby, Z.; and Maida, A. S. 2018 · 2018
Cited alongside, same era.
An optimization view on dynamic routing between capsules
Wang, D.; and Liu, Q. 2018 · 2018
Cited alongside, same era.
Spatio-temporal backpropagation for training high-performance spiking neural networks
Wu, Y.; Deng, L.; Li, G.; Zhu, J.; and Shi, L. 2018 · 2018
Cited alongside, same era.
A plasticity-centric approach to train the non-differential spiking neural networks
Zhang, T.; Zeng, Y.; Zhao, D.; and Shi, M. 2018 · 2018
Cited alongside, same era.
A brain-inspired decision-making spiking neural network and its application in unmanned aerial vehicle
Zhao, F.; Zeng, Y.; and Xu, B. 2018 · 2018
Cited alongside, same era.
LISNN: Improving Spiking Neural Networks with Lateral Interactions for Robust Object Recognition
Cheng, X.; Hao, Y.; Xu, J.; and Xu, B. 2020 · 2020
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Group Feedback Capsule Network
Ding, X.; Wang, N.; Gao, X.; Li, J.; Wang, X.; and Liu, T. 2020 · 2020
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A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule
Hao, Y.; Huang, X.; Dong, M.; and Xu, B. 2020 · 2020
Later among the works it cites.
Robustness to Noisy Synaptic Weights in Spiking Neural Networks
Li, C.; Chen, R.; Moutafis, C.; and Furber, S. 2020 · 2020
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Capsule routing via variational bayes
Ribeiro, F. D. S.; Leontidis, G.; and Kollias, S. 2020 · 2020
Later among the works it cites.
Brain inspired sequences production by spiking neural networks with reward-modulated stdp
Fang, H.; Zeng, Y.; and Zhao, F. 2021 · 2021
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Efficient-CapsNet: Capsule Network with Self-Attention Routing
Mazzia, V.; Salvetti, F.; and Chiaberge, M. 2021 · 2021
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Quantum superposition inspired spiking neural network
Sun, Y.; Zeng, Y.; and Zhang, T. 2021 · 2021
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Fast dynamic routing based on weighted kernel density estimation
Zhang, S.; Zhao, W.; Wu, X.; and Zhou, Q. 2021 · 2021
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Zhao, D.; Zeng, Y.; and Li, Y. 2021 · 2021
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