Fetching the paper…
Reading the bibliography…
Spiking neural networks (SNNs) have great potential for energy-efficient implementation of Deep Neural Networks (DNNs) on dedicated neuromorphic hardware.
Closing the Accuracy Gap in an Event-Based Visual Recognition Task
Rückauer, B.; Känzig, N.; Liu, S.-C.; Delbruck, T.; and Sandamirskaya, Y. 2019 · 1906
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y.; Bottou, L.; Bengio, Y.; and Haffner, P. 1998 · 1998
Earlier work this paper cites.
On the computational power of circuits of spiking neurons
Maass, W.; and Markram, H. 2004 · 2004
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012 · 2012
Earlier work this paper cites.
The arcade learning environment: An evaluation platform for general agents
Bellemare, M. G.; Naddaf, Y.; Veness, J.; and Bowling, M. 2013 · 2013
Earlier work this paper cites.
Mapping from frame-driven to frame-free event-driven vision systems by low-rate rate coding and coincidence processing–application to feedforward ConvNets
Pérez-Carrasco, J. A.; Zhao, B.; Serrano, C.; Acha, B.; Serrano-Gotarredona, T.; Chen, S.; and Linares-Barranco, B. 2013 · 2013
Earlier work this paper cites.
A million spiking-neuron integrated circuit with a scalable communication network and interface
Merolla, P. A.; Arthur, J. V.; Alvarez-Icaza, R.; Cassidy, A. S.; Sawada, J.; Akopyan, F.; Jackson, B. L.; Imam, N.; Guo, C.; Nakamura, Y.; et al. 2014 · 2014
Earlier work this paper cites.
Spiking deep convolutional neural networks for energy-efficient object recognition
Cao, Y.; Chen, Y.; and Khosla, D. 2015 · 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.; Liu, S.-C.; and Pfeiffer, M. 2015 · 2015
Earlier work this paper cites.
Deep learning
LeCun, Y.; Bengio, Y.; and Hinton, G. 2015 · 2015
Earlier work this paper cites.
Human-level control through deep reinforcement learning
Mnih, V.; Kavukcuoglu, K.; Silver, D.; Rusu, A. A.; Veness, J.; Bellemare, M. G.; Graves, A.; Riedmiller, M.; Fidjeland, A. K.; Ostrovski, G.; et al. 2015 · 2015
Cited alongside, same era.
Imagenet large scale visual recognition challenge
Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; et al. 2015 · 2015
Cited alongside, same era.
Wide & deep learning for recommender systems
Cheng, H.-T.; Koc, L.; Harmsen, J.; Shaked, T.; Chandra, T.; Aradhye, H.; Anderson, G.; Corrado, G.; Chai, W.; Ispir, M.; et al. 2016 · 2016
Cited alongside, same era.
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 · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Cited alongside, same era.
Bindsnet: A machine learning-oriented spiking neural networks library in python
Hazan, H.; Saunders, D. J.; Khan, H.; Patel, D.; Sanghavi, D. T.; Siegelmann, H. T.; and Kozma, R. 2018 · 2018
Later among the works it cites.
STDP-based spiking deep convolutional neural networks for object recognition
Kheradpisheh, S. R.; Ganjtabesh, M.; Thorpe, S. J.; and Masquelier, T. 2018 · 2018
Later among the works it cites.
Mozafari, M.; Ganjtabesh, M.; Nowzari-Dalini, A.; Thorpe, S. J.; and Masquelier, T. 2018 · 2018
Later among the works it cites.
Conversion of analog to spiking neural networks using sparse temporal coding
Rueckauer, B.; and Liu, S.-C. 2018 · 2018
Later among the works it cites.
Improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to Atari Breakout game
Patel, D.; Hazan, H.; Saunders, D. J.; Siegelmann, H. T.; and Kozma, R. 2019 · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Training deep spiking neural networks using backpropagation
Lee, J. H.; Delbruck, T.; and Pfeiffer, M. 2016 · 2016
Cited alongside, same era.
O’Connor, P.; and Welling, M. 2016 · 2016
Cited alongside, same era.
Event-driven random back-propagation: Enabling neuromorphic deep learning machines
Neftci, E. O.; Augustine, C.; Paul, S.; and Detorakis, G. 2017 · 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 · 2017
Cited alongside, same era.
Loihi: A neuromorphic manycore processor with on-chip learning
Davies, M.; Srinivasa, N.; Lin, T.-H.; Chinya, G.; Cao, Y.; Choday, S. H.; Dimou, G.; Joshi, P.; Imam, N.; Jain, S.; et al. 2018 · 2018
Cited alongside, same era.
Later among the works it cites.
Going deeper in spiking neural networks: Vgg and residual architectures
Sengupta, A.; Ye, Y.; Wang, R.; Liu, C.; and Roy, K. 2019 · 2019
Later among the works it cites.
BP-STDP: Approximating backpropagation using spike timing dependent plasticity
Tavanaei, A.; and Maida, A. 2019 · 2019
Later among the works it cites.
Grandmaster level in StarCraft II using multi-agent reinforcement learning
Vinyals, O.; Babuschkin, I.; Czarnecki, W. M.; Mathieu, M.; Dudzik, A.; Chung, J.; Choi, D. H.; Powell, R.; Ewalds, T.; Georgiev, P.; et al. 2019 · 2019
Later among the works it cites.
Direct training for spiking neural networks: Faster, larger, better
Wu, Y.; Deng, L.; Li, G.; Zhu, J.; Xie, Y.; and Shi, L. 2019 · 2019
Later among the works it cites.
Enabling spike-based backpropagation for training deep neural network architectures
Lee, C.; Sarwar, S. S.; Panda, P.; Srinivasan, G.; and Roy, K. 2020 · 2020
Closest in time.