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Spiking Neural Network (SNN) has been recognized as one of the next generation of neural networks.
A quantitative description of membrane current and its application to conduction and excitation in nerve
Hodgkin, A. L. and Huxley, A. F · 1952
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Spike-frequency adaptation of a generalized leaky integrate-and-fire model neuron
Liu, Y.-H. and Wang, X.-J · 2001
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Stochastic resonance in a sinusoidally forced lif model with noisy threshold
Barbi, M., Chillemi, S., Di Garbo, A., and Reale, L · 2003
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Simple model of spiking neurons
Izhikevich, E. M · 2003
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
Bengio, Y., Léonard, N., and Courville, A · 2013
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Spiking deep convolutional neural networks for energy-efficient object recognition
Cao, Y., Chen, Y., and Khosla, D · 2015
Earlier work this paper cites.
Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
Diehl, P. U., Neil, D., Binas, J., Cook, M., and Liu, S. C · 2015
Earlier work this paper cites.
Conversion of artificial recurrent neural networks to spiking neural networks for low-power neuromorphic hardware
Diehl, P. U., Zarrella, G., Cassidy, A., Pedroni, B. U., and Neftci, E · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Jian, S · 2016
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Sgdr: Stochastic gradient descent with warm restarts
Loshchilov, I. and Hutter, F · 2016
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Theory and tools for the conversion of analog to spiking convolutional neural networks
Rueckauer, B., Lungu, I.-A., Hu, Y., and Pfeiffer, M · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
Cited alongside, same era.
A low power, fully event-based gesture recognition system
Amir, A., Taba, B., Berg, D., Melano, T., McKinstry, J., Di Nolfo, C., Nayak, T., Andreopoulos, A., Garreau, G., Mendoza, M., et al · 2017
Cited alongside, same era.
Improved regularization of convolutional neural networks with cutout
DeVries, T. and Taylor, G. W · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H · 2017
Cited alongside, same era.
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
Research on learning algorithm of spiking neural network
LI, S.-L. and LI, J.-P · 2019
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Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks
Neftci, E. O., Mostafa, H., and Zenke, F · 2019
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Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation
Rathi, N., Srinivasan, G., Panda, P., and Roy, K · 2019
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Scaling deep spiking neural networks with binary stochastic activations
Roy, D., Chakraborty, I., and Roy, K · 2019
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Deep learning in spiking neural networks
Tavanaei, A., Ghodrati, M., Kheradpisheh, S. R., Masquelier, T., and Maida, A · 2019
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Rethinking the performance comparison between snns and anns
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Cited alongside, same era.
Stdp-based spiking deep convolutional neural networks for object recognition
Kheradpisheh, S. R., Ganjtabesh, M., Thorpe, S. J., and Masquelier, T · 2018
Cited alongside, same era.
Going deeper in spiking neural networks: Vgg and residual architectures
Sengupta, A., Ye, Y., Wang, R., Liu, C., and Roy, K · 2018
Cited alongside, same era.
Slayer: Spike layer error reassignment in time
Shrestha, S. B. and Orchard, G · 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
Cited alongside, same era.
Autoaugment: Learning augmentation strategies from data
Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q. V · 2019
Cited alongside, same era.
Bag of tricks for image classification with convolutional neural networks
He, T., Zhang, Z., Zhang, H., Zhang, Z., Xie, J., and Li, M · 2019
Cited alongside, same era.
Spiking-yolo: Spiking neural network for energy-efficient object detection
Kim, S., Park, S., Na, B., and Yoon, S · 2019
Cited alongside, same era.
Deng, L., Wu, Y., Hu, X., Liang, L., Ding, Y., Li, G., Zhao, G., Li, P., and Xie, Y · 2020
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Deep spiking neural network: Energy efficiency through time based coding
Han, B. and Roy, K · 2020
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Rmp-snn: Residual membrane potential neuron for enabling deeper high-accuracy and low-latency spiking neural network
Han, B., Srinivasan, G., and Roy, K · 2020
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Classifying neuromorphic datasets with tempotron and spike timing dependent plasticity
Iyer, L. R. and Chua, Y · 2020
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Designing network design spaces
Radosavovic, I., Kosaraju, R. P., Girshick, R., He, K., and Dollár, P · 2020
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Rathi, N. and Roy, K · 2020
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Optimal conversion of conventional artificial neural networks to spiking neural networks
Deng, S. and Gu, S · 2021
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