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Configuring deep Spiking Neural Networks (SNNs) is an exciting research avenue for low power spike event based computation.
Tidigits speech corpus
R Gary Leonard and George Doddington · 1993
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Time structure of the activity in neural network models
Wulfram Gerstner · 1995
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Lower bounds for the computational power of networks of spiking neurons
Wolfgang Maass · 1996
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Noisy spiking neurons with temporal coding have more computational power than sigmoidal neurons
Wolfgang Maass · 1996
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On the complexity of learning for a spiking neuron
Wolfgang Maass and Michael Schmitt · 1997
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Error-backpropagation in temporally encoded networks of spiking neurons
Sander M. Bohte, Joost N. Kok, and Han La Poutre · 2002
Earlier work this paper cites.
Spiking neuron models: Single neurons, populations, plasticity
Wulfram Gerstner and Werner M Kistler · 2002
Earlier work this paper cites.
The spike response model: a framework to predict neuronal spike trains
Renaud Jolivet, J Timothy, and Wulfram Gerstner · 2003
Earlier work this paper cites.
Extending SpikeProp
Benjamin Schrauwen and Jan Van Campenhout · 2004
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Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model
Jonathan W. Pillow, Liam Paninski, Valerie J. Uzzell, Eero P. Simoncelli, and E. J. Chichilnisky · 2005
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The tempotron: a neuron that learns spike timing–based decisions
Robert Gütig and Haim Sompolinsky · 2006
Earlier work this paper cites.
Fast modifications of the SpikeProp algorithm
Sam McKennoch, Dingding Liu, and Linda G Bushnell · 2006
Earlier work this paper cites.
On similarity measures for spike trains
Justin Dauwels, François Vialatte, Theophane Weber, and Andrzej Cichocki · 2008
Earlier work this paper cites.
Supervised learning in spiking neural networks with ReSuMe: Sequence learning, classification, and spike shifting
Filip Ponulak and Andrzej Kasinski · 2009
Cited alongside, same era.
Speaker-independent isolated digit recognition using an aer silicon cochlea
M. Abdollahi and S. C. Liu · 2011
Cited alongside, same era.
Span: Spike pattern association neuron for learning spatio-temporal spike patterns
Ammar Mohemmed, Stefan Schliebs, Satoshi Matsuda, and Nikola Kasabov · 2012
Cited alongside, same era.
Real-time classification and sensor fusion with a spiking deep belief network
Peter O’Connor, Daniel Neil, Shih-Chii Liu, Tobi Delbruck, and Michael Pfeiffer · 2013
Cited alongside, same era.
A million spiking-neuron integrated circuit with a scalable communication network and interface
Paul A. Merolla, John V. Arthur, Rodrigo Alvarez-Icaza, Andrew S. Cassidy, Jun Sawada, Filipp Akopyan, Bryan L. Jackson, Nabil Imam, Chen Guo, Yutaka Nakamura, Bernard Brezzo, Ivan Vo, Steven K. Esser, Rathinakumar Appuswamy, Brian Taba, Arnon Amir, Myron D. Flickner, William P. Risk, Rajit Manohar, and Dharmendra S. Modha · 2014
Convolutional networks for fast, energy-efficient neuromorphic computing
Steven K. Esser, Paul A. Merolla, John V. Arthur, Andrew S. Cassidy, Rathinakumar Appuswamy, Alexander Andreopoulos, David J. Berg, Jeffrey L. McKinstry, Timothy Melano, Davis R. Barch, Carmelo di Nolfo, Pallab Datta, Arnon Amir, Brian Taba, Myron D. Flickner, and Dharmendra S. Modha · 2016
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Truehappiness: Neuromorphic emotion recognition on truenorth
Peter U. Diehl, Bruno U. Pedroni, Andrew S. Cassidy, Paul Merolla, Emre Neftci, and Guido Zarrella · 2016
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Skimming digits: Neuromorphic classification of spike-encoded images
Gregory K. Cohen, Garrick Orchard, Sio-Hoi Leng, Jonathan Tapson, Ryad B. Benosman, and Andre van Schaik · 2016
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Robust spike-train learning in spike-event based weight update
Sumit Bam Shrestha and Qing Song · 2017
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Superspike: Supervised learning in multi-layer spiking neural networks
Friedemann Zenke and Surya Ganguli · 2017
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Cited alongside, same era.
The spinnaker project
Steve B. Furber, Francesco Galluppi, Steve Temple, and Luis A. Plana · 2014
Cited alongside, same era.
DL-ReSuMe: a delay learning-based remote supervised method for spiking neurons
Aboozar Taherkhani, Ammar Belatreche, Yuhua Li, and Liam P Maguire · 2015
Cited alongside, same era.
Backpropagation for energy-efficient neuromorphic computing
Steve K. Esser, Rathinakumar Appuswamy, Paul Merolla, John V. Arthur, and Dharmendra S. Modha · 2015
Cited alongside, same era.
Spiking deep networks with LIF neurons
Eric Hunsberger and Chris Eliasmith · 2015
Cited alongside, same era.
Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
Peter U. Diehl, Daniel Neil, Jonathan Binas, Matthew Cook, Shih-Chii Liu, and Michael Pfeiffer · 2015
Cited alongside, same era.
Converting static image datasets to spiking neuromorphic datasets using saccades
Garrick Orchard, Ajinkya Jayawant, Gregory K. Cohen, and Nitish Thakor · 2015
Cited alongside, same era.
Unsupervised regenerative learning of hierarchical features in spiking deep networks for object recognition
Priyadarshini Panda and Kaushik Roy · 2016
Cited alongside, same era.
Later among the works it cites.
Noisy softplus: an activation function that enables snns to be trained as anns
Qian Liu, Yunhua Chen, and Steve B. Furber · 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
Later among the works it cites.
DART: distribution aware retinal transform for event-based cameras
Bharath Ramesh, Hong Yang, Garrick Orchard, Ngoc Anh Le Thi, and Cheng Xiang · 2017
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A low power, fully event-based gesture recognition system
Arnon Amir, Brian Taba, David Berg, Timothy Melano, Jeffrey McKinstry, Carmelo di Nolfo, Tapan Nayak, Alexander Andreopoulos, Guillaume Garreau, Marcela Mendoza, Jeff Kusnitz, Michael Debole, Steve Esser, Tobi Delbruck, Myron Flickner, and Dharmendra Modha · 2017
Later among the works it cites.
Bio-inspired multi-layer spiking neural network extracts discriminative features from speech signals
Amirhossein Tavanaei and Anthony Maida · 2017
Later among the works it cites.
Loihi: A neuromorphic manycore processor with on-chip learning
Mike Davies, Narayan Srinivasa, Tsung-Han Lin, Gautham Chinya, Yongqiang Cao, Sri Harsha Choday, Georgios Dimou, Prasad Joshi, Nabil Imam, Shweta Jain, et al · 2018
Closest in time.
A biologically plausible speech recognition framework based on spiking neural networks
Jibin Wu, Yansong Chua, and Haizhou Li · 2018
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