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The vast majority of natural sensory data is temporally redundant.
Oversampling delta-sigma data converters: theory, design, and simulation
James C Candy and Gabor C Temes · 1962
Earlier work this paper cites.
Spikeprop: backpropagation for networks of spiking neurons
Sander M Bohte, Joost N Kok, and Johannes A La Poutré · 2000
Earlier work this paper cites.
A 128 × \times 128 120 db 15 μ \mu s latency asynchronous temporal contrast vision sensor
Patrick Lichtsteiner, Christoph Posch, and Tobi Delbruck · 2008
Earlier work this paper cites.
Spike-timing-dependent plasticity: a comprehensive overview
Henry Markram, Wulfram Gerstner, and Per Jesper Sjöström · 2012
Earlier work this paper cites.
1.1 computing’s energy problem (and what we can do about it)
Mark Horowitz · 2014
Cited alongside, same era.
An objective function for stdp
Yoshua Bengio, Thomas Mesnard, Asja Fischer, Saizheng Zhang, and Yuhai Wu · 2015
Cited alongside, same era.
Binaryconnect: Training deep neural networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 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.
Peter O’Connor and Max Welling
Cited in the paper.
Sigma delta quantized networks
Peter O’Connor and Max Welling
Cited in the paper.
Deep counter networks for asynchronous event-based processing
Jonathan Binas, Giacomo Indiveri, and Michael Pfeiffer · 2016
Later among the works it cites.
Training deep spiking neural networks using backpropagation
Jun Haeng Lee, Tobi Delbruck, and Michael Pfeiffer · 2016
Later among the works it cites.
Fast and efficient asynchronous neural computation with adapting spiking neural networks
Davide Zambrano and Sander M Bohte · 2016
Later among the works it cites.
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