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An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain.
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Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate, and dendritic location
M. Graupner and N. Brunel · 2012
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Pierre Baldi and Peter J Sadowski · 2013
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Neuromorphic electronic circuits for building autonomous cognitive systems
E. Chicca, F. Stefanini, and G. Indiveri · 2013
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Pylearn2: a machine learning research library
Ian J. Goodfellow, David Warde-Farley, Pascal Lamblin, Vincent Dumoulin, Mehdi Mirza, Razvan Pascanu, James Bergstra, Frédéric Bastien, and Yoshua Bengio · 2013
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Restricted boltzmann machines and continuous-time contrastive divergence in spiking neuromorphic systems, May 2013
E. Neftci, S. Das, B. Pedroni, K. Kreutz-Delgado, and G Cauwenberghs · 2013
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Event-driven contrastive divergence for spiking neuromorphic systems
E. Neftci, S. Das, B. Pedroni, K. Kreutz-Delgado, and G. Cauwenberghs · 2013
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Event-driven contrastive divergence for spiking neuromorphic systems
E. Neftci, S. Das, B. Pedroni, K. Kreutz-Delgado, and G. Cauwenberghs · 2013
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Spiking deep networks with lif neurons
Eric Hunsberger and Chris Eliasmith · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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How important is weight symmetry in backpropagation?
Qianli Liao, Joel Z Leibo, and Tomaso Poggio · 2015
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Energy-efficient neuromorphic classifiers
Daniel Marti, Mattia Rigotti, Mingoo Seok, and Stefano Fusi · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity
B. Nessler, E. Pfeiffer, and W. Maass · 2013
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Real-time classification and sensor fusion with a spiking deep belief network
P O’Connor, D. Neil, S.-C. Liu, T. Delbruck, and M. Pfeiffer · 2013
Cited alongside, same era.
Stochastic inference with deterministic spiking neurons
Mihai A Petrovici, Johannes Bill, Ilja Bytschok, Johannes Schemmel, and Karlheinz Meier · 2013
Cited alongside, same era.
Regularization of neural networks using dropconnect
Li Wan, Matthew Zeiler, Sixin Zhang, Yann L Cun, and Rob Fergus · 2013
Cited alongside, same era.
Spiking deep convolutional neural networks for energy-efficient object recognition
Yongqiang Cao, Yang Chen, and Deepak Khosla · 2014
Cited alongside, same era.
Low precision arithmetic for deep learning
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2014
Cited alongside, same era.
A framework for plasticity implementation on the spinnaker neural architecture
Francesco Galluppi, Xavier Lagorce, Evangelos Stromatias, Michael Pfeiffer, Luis A Plana, Steve B Furber, and Ryad Benjamin Benosman · 2014
Cited alongside, same era.
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A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128k synapses
Ning Qiao, Hesham Mostafa, Federico Corradi, Marc Osswald, Fabio Stefanini, Dora Sumislawska, and Giacomo Indiveri · 2015
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Learning in the machine: Random backpropagation and the learning channel
Pierre Baldi, Peter Sadowski, and Zhiqin Lu · 2016
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Binarynet: Training deep neural networks with weights and activations constrained to+ 1 or-1
Matthieu Courbariaux and Yoshua Bengio · 2016
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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, et al · 2016
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Learning in silicon beyond stdp: A neuromorphic implementation of multi-factor synaptic plasticity with calcium-based dynamics
F. L. Maldonado Huayaney, S. Nease, and E. Chicca · 2016
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What learning systems do intelligent agents need? complementary learning systems theory updated
Dharshan Kumaran, Demis Hassabis, and James L McClelland · 2016
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Training deep spiking neural networks using backpropagation
Jun Haeng Lee, Tobi Delbruck, and Michael Pfeiffer · 2016
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Random synaptic feedback weights support error backpropagation for deep learning
Timothy P Lillicrap, Daniel Cownden, Douglas B Tweed, and Colin J Akerman · 2016
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Supervised learning based on temporal coding in spiking neural networks
Hesham Mostafa · 2016
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Neural sampling by irregular gating inhibition of spiking neurons and attractor networks
Lorenz K Muller and Giacomo Indiveri · 2016
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Event-driven random back-propagation: Enabling neuromorphic deep learning machines
Emre Neftci, Charles Augustine, Somnath Paul, and Georgios Detorakis · 2016
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Direct feedback alignment provides learning in deep neural networks
Arild Nø kland · 2016
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Peter O’Connor and Max Welling · 2016
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Forward table-based presynaptic event-triggered spike-timing-dependent plasticity
Bruno U Pedroni, Sadique Sheik, Siddharth Joshi, Georgios Detorakis, Somnath Paul, Charles Augustine, Emre Neftci, and Gert Cauwenberghs · 2016
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Normalizing the normalizers: Comparing and extending network normalization schemes
Mengye Ren, Renjie Liao, Raquel Urtasun, Fabian H Sinz, and Richard S Zemel · 2016
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Membrane-dependent neuromorphic learning rule for unsupervised spike pattern detection
S. Sheik, S. Paul, C. Augustine, C. Kothapalli, and G. Cauwenberghs · 2016
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Synaptic sampling in hardware spiking neural networks
S. Sheik, S. Paul, C. Augustine, C. Kothapalli, G. Cauwenberghs, and E. Neftci · 2016
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Onac: Optimal number of active cores detector for energy efficient gpu computing
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