Fetching the paper…
Reading the bibliography…
Compared with artificial neural networks (ANNs), spiking neural networks (SNNs) are promising to explore the brain-like behaviors since the spikes could encode more spatio-temporal information.
P. J. Werbos, “Backpropagation through time: what it does and how to do it,” Proceedings of the IEEE , vol. 78, no. 10, pp. 1550–1560, 1990
1990
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
F. A. Gers, J. Schmidhuber, and F. Cummins, “Learning to forget: continual prediction with lstm,” Neural Computation , vol. 12, no. 10, p. 2451, 1999
1999
Earlier work this paper cites.
P. Y. Simard, D. Steinkraus, and J. C. Platt, “Best practices for convolutional neural networks applied to visual document analysis,” in International Conference on Document Analysis and Recognition , 2003, p. 958
2003
Earlier work this paper cites.
P. Lichtsteiner, C. Posch, and T. Delbruck, “A 128x128 120db 15us latency asynchronous temporal contrast vision sensor,” IEEE Journal of Solid-State Circuits , vol. 43, no. 2, pp. 566–576, 2007
2007
Earlier work this paper cites.
J. N. Allen, H. S. Abdel-Aty-Zohdy, and R. L. Ewing, “Cognitive processing using spiking neural networks,” in IEEE 2009 National Aerospace and Electronics Conference , 2009, pp. 56–64
2009
Earlier work this paper cites.
G. Hinton, L. Deng, D. Yu, and G. E. Dahl, “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,” IEEE Signal Processing Magazine , vol. 29, no. 6, pp. 82–97, 2012
2012
Earlier work this paper cites.
X. Zhang, Z. Xu, C. Henriquez, and S. Ferrari, “Spike-based indirect training of a spiking neural network-controlled virtual insect,” in Decision and Control (CDC), 2013 IEEE 52nd Annual Conference on . IEEE, 2013, pp. 6798–6805
2013
Earlier work this paper cites.
D. Querlioz, O. Bichler, P. Dollfus, and C. Gamrat, “Immunity to device variations in a spiking neural network with memristive nanodevices,” IEEE Transactions on Nanotechnology , vol. 12, no. 3, pp. 288–295, 2013
2013
Earlier work this paper cites.
J. A. Perezcarrasco, B. Zhao, C. Serrano, B. Acha, T. Serranogotarredona, S. Chen, and B. Linaresbarranco, “Mapping from frame-driven to frame-free event-driven vision systems by low-rate rate-coding and coincidence processing. application to feed forward convnets.” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 35, no. 11, pp. 2706–19, 2013
2013
Earlier work this paper cites.
O. Peter, N. Daniel, S. C. Liu, D. Tobi, and P. Michael, “Real-time classification and sensor fusion with a spiking deep belief network,” Frontiers in Neuroscience , vol. 7, p. 178, 2013
2013
Earlier work this paper cites.
E. Neftci, S. Das, B. Pedroni, K. Kreutzdelgado, and G. Cauwenberghs, “Event-driven contrastive divergence for spiking neuromorphic systems,” Frontiers in Neuroscience , vol. 7, p. 272, 2013
2013
Cited alongside, same era.
L. Deng and D. Yu, “Deep learning: Methods and applications,” Foundations and Trends in Signal Processing , vol. 7, no. 3, pp. 197–387, 2014
2014
Cited alongside, same era.
Jia, Yangqing, Shelhamer, Evan, Donahue, Jeff, Karayev, Sergey, Long, and Jonathan, “Caffe: Convolutional architecture for fast feature embedding,” Eprint Arxiv , pp. 675–678, 2014
2014
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition . Springer International Publishing, 2014
2014
Cited alongside, same era.
Y. Bengio, T. Mesnard, A. Fischer, S. Zhang, and Y. Wu, “An objective function for stdp,” Computer Science , 2015
2015
Later among the works it cites.
G. Orchard, A. Jayawant, G. K. Cohen, and N. Thakor, “Converting static image datasets to spiking neuromorphic datasets using saccades,” Frontiers in Neuroscience , vol. 9, 2015
2015
Later among the works it cites.
T. Hwu, J. Isbell, N. Oros, and J. Krichmar, “A self-driving robot using deep convolutional neural networks on neuromorphic hardware,” arXiv.org , 2016
2016
Later among the works it cites.
S. K. Esser, P. A. Merolla, J. V. Arthur, A. S. Cassidy, R. Appuswamy, A. Andreopoulos, D. J. Berg, J. L. Mckinstry, T. Melano, and D. R. Barch, “Convolutional networks for fast, energy-efficient neuromorphic computing,” Proceedings of the National Academy of Sciences of the United States of America , vol. 113, no. 41, p. 11441, 2016
2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
B. V. Benjamin, P. Gao, E. Mcquinn, S. Choudhary, A. R. Chandrasekaran, J. M. Bussat, R. Alvarez-Icaza, J. V. Arthur, P. A. Merolla, and K. Boahen, “Neurogrid: A mixed-analog-digital multichip system for large-scale neural simulations,” Proceedings of the IEEE , vol. 102, no. 5, pp. 699–716, 2014
2014
Cited alongside, same era.
P. A. Merolla, J. V. Arthur, R. Alvarezicaza, A. S. Cassidy, J. Sawada, F. Akopyan, B. L. Jackson, N. Imam, C. Guo, and Y. Nakamura, “Artificial brains. a million spiking-neuron integrated circuit with a scalable communication network and interface.” Science , vol. 345, no. 6197, pp. 668–73, 2014
2014
Cited alongside, same era.
S. B. Furber, F. Galluppi, S. Temple, and L. A. Plana, “The spinnaker project,” Proceedings of the IEEE , vol. 102, no. 5, pp. 652–665, 2014
2014
Cited alongside, same era.
N. Kasabov and E. Capecci, “Spiking neural network methodology for modelling, classification and understanding of eeg spatio-temporal data measuring cognitive processes,” Information Sciences , vol. 294, no. C, pp. 565–575, 2015
2015
Cited alongside, same era.
P. U. Diehl and M. Cook, “Unsupervised learning of digit recognition using spike-timing-dependent plasticity,” Frontiers in Computational Neuroscience , vol. 9, p. 99, 2015
2015
Cited alongside, same era.
P. U. Diehl, D. Neil, J. Binas, and M. Cook, “Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing,” in International Joint Conference on Neural Networks , 2015, pp. 1–8
2015
Cited alongside, same era.
E. Hunsberger and C. Eliasmith, “Spiking deep networks with lif neurons,” Computer Science , 2015
2015
Cited alongside, same era.
J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Gated feedback recurrent neural networks,” Computer Science , pp. 2067–2075, 2015
2015
Cited alongside, same era.
S. S. Zhang, L.P. Shi, “Creating more intelligent robots through brain-inspired computing,” Science(suppl) , vol. 354, 2016
2016
Later among the works it cites.
S. R. Kheradpisheh, M. Ganjtabesh, and T. Masquelier, “Bio-inspired unsupervised learning of visual features leads to robust invariant object recognition,” Neurocomputing , vol. 205, no. C, pp. 382–392, 2016
2016
Later among the works it cites.
P. O’Connor and M. Welling, “Deep spiking networks,” arXiv.org , 2016
2016
Later among the works it cites.
J. H. Lee, T. Delbruck, and M. Pfeiffer, “Training deep spiking neural networks using backpropagation,” Frontiers in Neuroscience , vol. 10, 2016
2016
Later among the works it cites.
D. Neil, M. Pfeiffer, and S. C. Liu, “Phased lstm: Accelerating recurrent network training for long or event-based sequences,” arXiv.org , 2016
2016
Later among the works it cites.
D. Neil and S. C. Liu, “Effective sensor fusion with event-based sensors and deep network architectures,” in IEEE Int. Symposium on Circuits and Systems , 2016
2016
Later among the works it cites.
G. K. Cohen, G. Orchard, S. H. Leng, J. Tapson, R. B. Benosman, and A. V. Schaik, “Skimming digits: Neuromorphic classification of spike-encoded images,” Frontiers in Neuroscience , vol. 10, no. 184, 2016
2016
Later among the works it cites.
P. Chaudhari and H. Agarwal, Progressive Review Towards Deep Learning Techniques . Springer Singapore, 2017
2017
Closest in time.