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Deep Spiking Neural Networks (SNNs) present optimization difficulties for gradient-based approaches due to discrete binary activation and complex spatial-temporal dynamics.
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Going deeper with convolutions
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2016
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Training deep spiking neural networks using backpropagation
Jun Haeng Lee, Tobi Delbruck, and Michael Pfeiffer · 2016
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Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
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Mastering the game of go with deep neural networks and tree search
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Cifar10-dvs: An event-stream dataset for object classification
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Technical report: supervised training of convolutional spiking neural networks with pytorch
Romain Zimmer, Thomas Pellegrini, Srisht Fateh Singh, and Timothée Masquelier · 2019
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Temporal coding in spiking neural networks with alpha synaptic function
Iulia M Comsa, Krzysztof Potempa, Luca Versari, Thomas Fischbacher, Andrea Gesmundo, and Jyrki Alakuijala · 2020
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Deep spiking neural network: Energy efficiency through time based coding
Bing Han and Kaushik Roy · 2020
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Rmp-snn: Residual membrane potential neuron for enabling deeper high-accuracy and low-latency spiking neural network
Bing Han, Gopalakrishnan Srinivasan, and Kaushik Roy · 2020
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Comparing snns and rnns on neuromorphic vision datasets: Similarities and differences
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SGDR: stochastic gradient descent with warm restarts
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Supervised learning based on temporal coding in spiking neural networks
Hesham Mostafa · 2017
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Conversion of continuous-valued deep networks to efficient event-driven networks for image classification
Bodo Rueckauer, Iulia-Alexandra Lungu, Yuhuang Hu, Michael Pfeiffer, and Shih-Chii Liu · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
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Yangfan Hu, Huajin Tang, Yueming Wang, and Gang Pan · 2018
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Gradient descent for spiking neural networks
Dongsung Huh and Terrence J Sejnowski · 2018
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Temporal backpropagation for spiking neural networks with one spike per neuron
Saeed Reza Kheradpisheh and Timothée Masquelier · 2020
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Unifying activation- and timing-based learning rules for spiking neural networks
Jinseok Kim, Kyungsu Kim, and Jae-Joon Kim · 2020
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Enabling spike-based backpropagation for training deep neural network architectures
Chankyu Lee, Syed Shakib Sarwar, Priyadarshini Panda, Gopalakrishnan Srinivasan, and Kaushik Roy · 2020
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Nitin Rathi and Kaushik Roy · 2020
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Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation
Nitin Rathi, Gopalakrishnan Srinivasan, Priyadarshini Panda, and Kaushik Roy · 2020
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Convolutional spiking neural networks for spatio-temporal feature extraction
Ali Samadzadeh, Fatemeh Sadat Tabatabaei Far, Ali Javadi, Ahmad Nickabadi, and Morteza Haghir Chehreghani · 2020
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Effective and efficient computation with multiple-timescale spiking recurrent neural networks
Bojian Yin, Federico Corradi, and Sander M Bohté · 2020
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Temporal spike sequence learning via backpropagation for deep spiking neural networks
Wenrui Zhang and Peng Li · 2020
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Optimal conversion of conventional artificial neural networks to spiking neural networks
Shikuang Deng and Shi Gu · 2021
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Incorporating learnable membrane time constant to enhance learning of spiking neural networks
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Low-latency spiking neural networks using pre-charged membrane potential and delayed evaluation
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A free lunch from ann: Towards efficient, accurate spiking neural networks calibration
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Optimized spiking neurons can classify images with high accuracy through temporal coding with two spikes
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The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks
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Going deeper with directly-trained larger spiking neural networks
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Temporal-coded deep spiking neural network with easy training and robust performance
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