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The Yin-Yang dataset was developed for research on biologically plausible error backpropagation and deep learning in spiking neural networks.
“Gradient-based learning applied to document recognition”
Yann LeCun, L“’eon Bottou, Yoshua Bengio and Patrick Haffner · 1998
Earlier work this paper cites.
“The kernel trick for distances”
Bernhard Scholkopf · 2001
Earlier work this paper cites.
“An event-based neural network architecture with an asynchronous programmable synaptic memory”
Saber Moradi and Giacomo Indiveri · 2013
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“Adam: A Method for Stochastic Optimization”
Diederik. Kingma and Jimmy Ba · 2014
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“Backpropagation for energy-efficient neuromorphic computing”
Steve Esser et al · 2015
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“Precise neural network computation with imprecise analog devices”
Jonathan Binas et al · 2016
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“Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms”
Han Xiao, Kashif Rasul and Roland Vollgraf · 2017
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“An accelerated analog neuromorphic hardware system emulating NMDA-and calcium-based non-linear dendrites”
Johannes Schemmel, Laura Kriener, Paul M“”uller and Karlheinz Meier · 2017
Earlier work this paper cites.
“Equilibrium propagation: Bridging the gap between energy-based models and backpropagation”
Benjamin Scellier and Yoshua Bengio · 2017
Cited alongside, same era.
“Neuromorphic hardware in the loop: Training a deep spiking network on the brainscales wafer-scale system”
Sebastian Schmitt et al · 2017
Cited alongside, same era.
“Towards deep learning with segregated dendrites”
Jordan Guerguiev, Timothy Lillicrap and Blake Richards · 2017
Cited alongside, same era.
“A 0.086-mm 2
Charlotte Frenkel, Martin Lefebvre, Jean-Didier Legat and David Bol · 2018
Cited alongside, same era.
“Dendritic cortical microcircuits approximate the backpropagation algorithm”
Jo“˜ao Sacramento, Rui Ponte, Yoshua Bengio and Walter Senn · 2018
Cited alongside, same era.
“An ultra-low power sigma-delta neuron circuit”
Manu Nair and Giacomo Indiveri · 2019
“Theories of error back-propagation in the brain”
James Whittington and Rafal Bogacz · 2019
Later among the works it cites.
“Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate”
Sebastian Billaudelle et al · 2020
Later among the works it cites.
“Surrogate gradients for analog neuromorphic computing”
Benjamin Cramer et al · 2020
Later among the works it cites.
“Yin-Yang dataset repository” Accessed: 2021-01-20, https://github.com/lkriener/yin_yang_data_set
2021
Closest in time.
“Fast and energy-efficient neuromorphic deep learning with first-spike times”
J G“”oltz et al · 2021
Closest in time.
“Event-based backpropagation can compute exact gradients for spiking neural networks”
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Cited alongside, same era.
“Fast and deep: energy-efficient neuromorphic learning with first-spike times”
Julian G“”oltz et al · 2019
Cited alongside, same era.
Timo Wunderlich and Christian Pehle · 2021
Closest in time.
“Latent Equilibrium: Arbitrarily fast computation with arbitrarily slow neurons”
Paul Haider et al · 2021
Closest in time.