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“A wafer-scale neuromorphic hardware system for large-scale neural modeling”
Johannes Schemmel et al · 1950
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“A wafer-scale neuromorphic hardware system for large-scale neural modeling”
Johannes Schemmel et al · 1950
Earlier work this paper cites.
“The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors”
Seppo Linnainmaa · 1970
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“The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors”
Seppo Linnainmaa · 1970
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“Applications of advances in nonlinear sensitivity analysis”
Paul Werbos · 1982
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“Applications of advances in nonlinear sensitivity analysis”
Paul Werbos · 1982
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“Learning representations by back-propagating errors”
David. Rumelhart, Geoffrey. Hinton and Ronald. Williams · 1986
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“Learning representations by back-propagating errors”
David. Rumelhart, Geoffrey. Hinton and Ronald. Williams · 1986
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“Neuromorphic electronic systems”
Carver Mead · 1990
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“Neuromorphic electronic systems”
Carver Mead · 1990
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“Speed of processing in the human visual system”
Simon Thorpe, Denis Fize and Catherine Marlot · 1996
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“Speed of processing in the human visual system”
Simon Thorpe, Denis Fize and Catherine Marlot · 1996
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“Spiking neurons”
Wulfram Gerstner · 1998
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“Gradient-based learning applied to document recognition”
Yann LeCun, L“’eon Bottou, Yoshua Bengio and Patrick Haffner · 1998
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“Spiking neurons”
Wulfram Gerstner · 1998
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“Gradient-based learning applied to document recognition”
Yann LeCun, L“’eon Bottou, Yoshua Bengio and Patrick Haffner · 1998
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“SpikeProp: backpropagation for networks of spiking neurons.”
Sander Bohte, Joost Kok and Johannes La“’e · 2000
Earlier work this paper cites.
“SpikeProp: backpropagation for networks of spiking neurons.”
Sander Bohte, Joost Kok and Johannes La“’e · 2000
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“What is different with spiking neurons?”
Wulfram Gerstner · 2001
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“Spike-based strategies for rapid processing”
Simon Thorpe, Arnaud Delorme and Rufin Van · 2001
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“What is different with spiking neurons?”
Wulfram Gerstner · 2001
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“Spike-based strategies for rapid processing”
Simon Thorpe, Arnaud Delorme and Rufin Van · 2001
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“Neocortical pyramidal cells respond as integrate-and-fire neurons to in vivo–like input currents”
Alexander Rauch et al · 2003
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“Neocortical pyramidal cells respond as integrate-and-fire neurons to in vivo–like input currents”
Alexander Rauch et al · 2003
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“Which model to use for cortical spiking neurons?”
Eugene Izhikevich · 2004
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“First spikes in ensembles of human tactile afferents code complex spatial fingertip events”
Roland Johansson and Ingvars Birznieks · 2004
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“Which model to use for cortical spiking neurons?”
Eugene Izhikevich · 2004
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“First spikes in ensembles of human tactile afferents code complex spatial fingertip events”
Roland Johansson and Ingvars Birznieks · 2004
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“The tempotron: a neuron that learns spike timing–based decisions”
Robert G“”utig and Haim Sompolinsky · 2006
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“The tempotron: a neuron that learns spike timing–based decisions”
Robert G“”utig and Haim Sompolinsky · 2006
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“Rapid neural coding in the retina with relative spike latencies”
Tim Gollisch and Markus Meister · 2008
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“Rapid neural coding in the retina with relative spike latencies”
Tim Gollisch and Markus Meister · 2008
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“How good are neuron models?”
Wulfram Gerstner and Richard Naud · 2009
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“How good are neuron models?”
Wulfram Gerstner and Richard Naud · 2009
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“A review on memristive devices and applications”
Themistoklis Prodromakis and Chris Toumazou · 2010
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“A review on memristive devices and applications”
Themistoklis Prodromakis and Chris Toumazou · 2010
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“Imagenet classification with deep convolutional neural networks”
Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton · 2012
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“Is the brain a good model for machine intelligence?”
Rodney Brooks, Demis Hassabis, Dennis Bray and Amnon Shashua · 2012
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“Imagenet classification with deep convolutional neural networks”
Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton · 2012
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“Is the brain a good model for machine intelligence?”
Rodney Brooks, Demis Hassabis, Dennis Bray and Amnon Shashua · 2012
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“Stochastic inference with deterministic spiking neurons”
Mihai Petrovici et al · 2013
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“Stochastic inference with deterministic spiking neurons”
Mihai Petrovici et al · 2013
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“Event-driven contrastive divergence for spiking neuromorphic systems”
Emre Neftci et al · 2014
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“Characterization and compensation of network-level anomalies in mixed-signal neuromorphic modeling platforms”
Mihai Petrovici et al · 2014
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“Event-driven contrastive divergence for spiking neuromorphic systems”
Emre Neftci et al · 2014
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“Characterization and compensation of network-level anomalies in mixed-signal neuromorphic modeling platforms”
Mihai Petrovici et al · 2014
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“Adam: A Method for Stochastic Optimization”
Diederik. Kingma and Jimmy Ba · 2014
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“Spiking deep convolutional neural networks for energy-efficient object recognition”
Yongqiang Cao, Yang Chen and Deepak Khosla · 2015
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“Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip”
Filipp Akopyan et al · 2015
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“Backpropagation for energy-efficient neuromorphic computing”
Steve Esser et al · 2015
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“Scalable energy-efficient, low-latency implementations of trained spiking deep belief networks on spinnaker”
Evangelos Stromatias et al · 2015
Earlier work this paper cites.
“Spiking deep convolutional neural networks for energy-efficient object recognition”
Yongqiang Cao, Yang Chen and Deepak Khosla · 2015
Earlier work this paper cites.
“Truenorth: Design and tool flow of a 65 mw 1 million neuron programmable neurosynaptic chip”
Filipp Akopyan et al · 2015
Earlier work this paper cites.
“Backpropagation for energy-efficient neuromorphic computing”
Steve Esser et al · 2015
Earlier work this paper cites.
“Scalable energy-efficient, low-latency implementations of trained spiking deep belief networks on spinnaker”
Evangelos Stromatias et al · 2015
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“What artificial intelligence can and can’t do right now”
Andrew Ng · 2016
Cited alongside, same era.
“Searching for principles of brain computation”
Wolfgang Maass · 2016
Cited alongside, same era.
“Conversion of artificial recurrent neural networks to spiking neural networks for low-power neuromorphic hardware”
Peter Diehl et al · 2016
Cited alongside, same era.
“Stochastic inference with spiking neurons in the high-conductance state”
Mihai Petrovici et al · 2016
Cited alongside, same era.
“Stochastic synapses enable efficient brain-inspired learning machines”
Emre Neftci et al · 2016
Cited alongside, same era.
“Training spiking deep networks for neuromorphic hardware”
Eric Hunsberger and Chris Eliasmith · 2016
“Training spiking convnets by stdp and gradient descent”
Amirhossein Tavanaei, Zachary Kirby and Anthony Maida · 2018
Later among the works it cites.
“A 4096-neuron 1M-synapse 3.8-pJ/SOP spiking neural network with on-chip STDP learning and sparse weights in 10-nm FinFET CMOS”
Gregory Chen et al · 2018
Later among the works it cites.
“An Accelerated LIF Neuronal Network Array for a Large-Scale Mixed-Signal Neuromorphic Architecture”
S.. Aamir et al · 2018
Later among the works it cites.
“Dendritic cortical microcircuits approximate the backpropagation algorithm”
Jo“˜ao Sacramento, Rui Ponte, Yoshua Bengio and Walter Senn · 2018
Later among the works it cites.
“A Mixed-Signal Structured AdEx Neuron for Accelerated Neuromorphic Cores”
S.. Aamir et al · 2018
Later among the works it cites.
“A deep learning framework for neuroscience”
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Cited alongside, same era.
“Demonstrating Hybrid Learning in a Flexible Neuromorphic Hardware System”
S. Friedmann et al · 2016
Cited alongside, same era.
“Form versus function: theory and models for neuronal substrates”
Mihai Petrovici · 2016
Cited alongside, same era.
“What artificial intelligence can and can’t do right now”
Andrew Ng · 2016
Cited alongside, same era.
“Searching for principles of brain computation”
Wolfgang Maass · 2016
Cited alongside, same era.
“Conversion of artificial recurrent neural networks to spiking neural networks for low-power neuromorphic hardware”
Peter Diehl et al · 2016
Cited alongside, same era.
“Stochastic inference with spiking neurons in the high-conductance state”
Mihai Petrovici et al · 2016
Cited alongside, same era.
Blake Richards et al · 2019
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“Benchmarks for progress in neuromorphic computing”
Mike Davies · 2019
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“Surrogate gradient learning in spiking neural networks”
Emre Neftci, Hesham Mostafa and Friedemann Zenke · 2019
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“Deep Spiking Neural Network with Spike Count based Learning Rule”
Jibin Wu et al · 2019
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“Towards spike-based machine intelligence with neuromorphic computing”
Kaushik Roy, Akhilesh Jaiswal and Priyadarshini Panda · 2019
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“Accelerated physical emulation of Bayesian inference in spiking neural networks”
Akos Kungl et al · 2019
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“Stochasticity from function—Why the Bayesian brain may need no noise”
Dominik Dold et al · 2019
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“Deterministic networks for probabilistic computing”
Jakob Jordan et al · 2019
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“Biologically plausible deep learning–but how far can we go with shallow networks?”
Bernd Illing, Wulfram Gerstner and Johanni Brea · 2019
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“Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate”
Sebastian Billaudelle et al · 2019
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“SpiNNaker 2: A 10 Million Core Processor System for Brain Simulation and Machine Learning”
Christian Mayr, Sebastian Hoeppner and Steve Furber · 2019
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“Towards artificial general intelligence with hybrid Tianjic chip architecture”
Jing Pei et al · 2019
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“Training Deep Networks with Time-to-First-Spike Coding on the BrainScaleS Wafer-Scale System”, 2019
Julian G“”oltz · 2019
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“Demonstrating advantages of neuromorphic computation: a pilot study”
Timo Wunderlich et al · 2019
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“All-optical spiking neurosynaptic networks with self-learning capabilities”
J Feldmann et al · 2019
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“PyTorch: An Imperative Style, High-Performance Deep Learning Library”
Adam Paszke et al · 2019
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“A deep learning framework for neuroscience”
Blake Richards et al · 2019
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“Benchmarks for progress in neuromorphic computing”
Mike Davies · 2019
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“Surrogate gradient learning in spiking neural networks”
Emre Neftci, Hesham Mostafa and Friedemann Zenke · 2019
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“Deep Spiking Neural Network with Spike Count based Learning Rule”
Jibin Wu et al · 2019
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“Towards spike-based machine intelligence with neuromorphic computing”
Kaushik Roy, Akhilesh Jaiswal and Priyadarshini Panda · 2019
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“Accelerated physical emulation of Bayesian inference in spiking neural networks”
Akos Kungl et al · 2019
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“Stochasticity from function—Why the Bayesian brain may need no noise”
Dominik Dold et al · 2019
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“Deterministic networks for probabilistic computing”
Jakob Jordan et al · 2019
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“Biologically plausible deep learning–but how far can we go with shallow networks?”
Bernd Illing, Wulfram Gerstner and Johanni Brea · 2019
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“Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate”
Sebastian Billaudelle et al · 2019
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“SpiNNaker 2: A 10 Million Core Processor System for Brain Simulation and Machine Learning”
Christian Mayr, Sebastian Hoeppner and Steve Furber · 2019
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“Towards artificial general intelligence with hybrid Tianjic chip architecture”
Jing Pei et al · 2019
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“Training Deep Networks with Time-to-First-Spike Coding on the BrainScaleS Wafer-Scale System”, 2019
Julian G“”oltz · 2019
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“Demonstrating advantages of neuromorphic computation: a pilot study”
Timo Wunderlich et al · 2019
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“All-optical spiking neurosynaptic networks with self-learning capabilities”
J Feldmann et al · 2019
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“PyTorch: An Imperative Style, High-Performance Deep Learning Library”
Adam Paszke et al · 2019
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“Language models are few-shot learners”
Tom Brown et al · 2020
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“S4NN: temporal backpropagation for spiking neural networks with one spike per neuron”
Saeed Kheradpisheh and Timoth“’ee Masquelier · 2020
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“Accelerated Analog Neuromorphic Computing”
Johannes Schemmel, Sebastian Billaudelle, Phillip Dauer and Johannes Weis · 2020
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“Temporal coding in spiking neural networks with alpha synaptic function”
Iulia Comsa et al · 2020
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“Training spiking multi-layer networks with surrogate gradients on an analog neuromorphic substrate”
Benjamin Cramer et al · 2020
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“Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits”
Alexandre Payeur et al · 2020
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“Extending BrainScaleS OS for BrainScaleS-2”
Eric Müller et al · 2020
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“Adam: A Method for Stochastic Optimization”
Diederik. Kingma and Jimmy Ba · 2020
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“Language models are few-shot learners”
Tom Brown et al · 2020
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“S4NN: temporal backpropagation for spiking neural networks with one spike per neuron”
Saeed Kheradpisheh and Timoth“’ee Masquelier · 2020
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“Accelerated Analog Neuromorphic Computing”
Johannes Schemmel, Sebastian Billaudelle, Phillip Dauer and Johannes Weis · 2020
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“Temporal coding in spiking neural networks with alpha synaptic function”
Iulia Comsa et al · 2020
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“Training spiking multi-layer networks with surrogate gradients on an analog neuromorphic substrate”
Benjamin Cramer et al · 2020
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“Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits”
Alexandre Payeur et al · 2020
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“Extending BrainScaleS OS for BrainScaleS-2”
Eric Müller et al · 2020
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Laura Kriener, Julian Göltz and Mihai. Petrovici · 2021
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Laura Kriener, Julian Göltz and Mihai. Petrovici · 2021
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