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Recurrent networks of spiking neurons (RSNNs) underlie the astounding computing and learning capabilities of the brain.
Developments of a water-maze procedure for studying spatial learning in the rat
Richard Morris · 1984
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Learning to learn using gradient descent
Sepp Hochreiter, A Steven Younger, and Peter R Conwell · 2001
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Synaptic theory of working memory
Gianluigi Mongillo, Omri Barak, and Misha Tsodyks · 2008
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Spike-based reinforcement learning in continuous state and action space: when policy gradient methods fail
Eleni Vasilaki, Nicolas Frémaux, Robert Urbanczik, Walter Senn, and Wulfram Gerstner · 2009
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A wafer-scale neuromorphic hardware system for large-scale neural modeling
Johannes Schemmel, Daniel Brüderle, Andreas Grübl, Matthias Hock, Karlheinz Meier, and Sebastian Millner · 2010
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Overview of the spinnaker system architecture
Steve B Furber, David R Lester, Luis A Plana, Jim D Garside, Eustace Painkras, Steve Temple, and Andrew D Brown · 2013
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How to build a brain: A neural architecture for biological cognition
Chris Eliasmith · 2013
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Neuronal dynamics: From single neurons to networks and models of cognition
Wulfram Gerstner, Werner M. Kistler, Richard Naud, and Liam Paninski · 2014
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Learning longer memory in recurrent neural networks
Tomas Mikolov, Armand Joulin, Sumit Chopra, Michael Mathieu, and Marc’Aurelio Ranzato · 2014
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Hierarchical process memory: memory as an integral component of information processing
Uri Hasson, Janice Chen, and Christopher J Honey · 2015
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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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Automated high-throughput characterization of single neurons by means of simplified spiking models
Christian Pozzorini, Skander Mensi, Olivier Hagens, Richard Naud, Christof Koch, and Wulfram Gerstner · 2015
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Network Plasticity as Bayesian Inference
David Kappel, Stefan Habenschuss, Robert Legenstein, and Wolfgang Maass · 2015
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A simple way to initialize recurrent networks of rectified linear units
Quoc V. Le, Navdeep Jaitly, and Geoffrey E. Hinton · 2015
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‘Activity-silent’ working memory in prefrontal cortex: a dynamic coding framework
Mark G. Stokes · 2015
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Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, 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, Carmelo di Nolfo, Pallab Datta, Arnon Amir, Brian Taba, Myron D. Flickner, and Dharmendra S. Modha · 2016
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Reward-based stochastic self-configuration of neural circuits
David Kappel, Robert Legenstein, Stefan Habenschuss, Michael Hsieh, and Wolfgang Maass · 2018
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Deep rewiring: Training very sparse deep networks
Guillaume Bellec, David Kappel, Wolfgang Maass, and Robert Legenstein · 2018
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Prefrontal cortex as a meta-reinforcement learning system
Jane X Wang, Zeb Kurth-Nelson, Dharshan Kumaran, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Demis Hassabis, and Matthew Botvinick · 2018
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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
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Cited alongside, same era.
Learning to reinforcement learn
Jane X Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, and Matt Botvinick · 2016
Cited alongside, same era.
R L 2 {RL}^{2} : Fast reinforcement learning via slow reinforcement learning
Yan Duan, John Schulman, Xi Chen, Peter L Bartlett, Ilya Sutskever, and Pieter Abbeel · 2016
Cited alongside, same era.
Using firing-rate dynamics to train recurrent networks of spiking model neurons
Brian DePasquale, Mark M Churchland, and LF Abbott · 2016
Cited alongside, same era.
Gradient descent for spiking neural networks
Dongsung Huh and Terrence J Sejnowski · 2017
Cited alongside, same era.
Supervised learning in spiking neural networks with force training
Wilten Nicola and Claudia Clopath · 2017
Cited alongside, same era.
Cortical microcircuits as gated-recurrent neural networks
Rui Costa, Ioannis Alexandros Assael, Brendan Shillingford, Nando de Freitas, and Tim Vogels · 2017
Cited alongside, same era.
LSTM: A search space odyssey
Klaus Greff, Rupesh K Srivastava, Jan Koutník, Bas R Steunebrink, and Jürgen Schmidhuber · 2017
Cited alongside, same era.
Computational properties of networks of spiking neurons with adapting neurons; in preparation
Guillaume Bellec, Darjan Salaj, Anand Subramoney, Robert Legenstein, and Wolfgang Maass · 2018
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Systematic generation of biophysically detailed models for diverse cortical neuron types
Nathan W Gouwens, Jim Berg, David Feng, Staci A Sorensen, Hongkui Zeng, Michael J Hawrylycz, Christof Koch, and Anton Arkhipov · 2018
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Generalized leaky integrate-and-fire models classify multiple neuron types
Corinne Teeter, Ramakrishnan Iyer, Vilas Menon, Nathan Gouwens, David Feng, Jim Berg, Aaron Szafer, Nicholas Cain, Hongkui Zeng, Michael Hawrylycz, et al · 2018
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Recurrent networks of spiking neurons learn to learn; in preparation
Anand Subramoney, Guillaume Bellec, Franz Scherr, Robert Legenstein, and Wolfgang Maass · 2018
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A neural population mechanism for rapid learning
Matthew G Perich, Juan A Gallego, and Lee E Miller · 2018
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© 2018 Allen Institute for Brain Science. Allen Cell Types Database, cell feature search. Available from:
Allen Institute · 2018
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