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Much of studies on neural computation are based on network models of static neurons that produce analog output, despite the fact that information processing in the brain is predominantly carried out by dynamic neurons that produce discrete pulses called spikes.
The mathematical theory of optimal processes
Lev Semenovich Pontryagin, EF Mishchenko, VG Boltyanskii, and RV Gamkrelidze · 1962
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Error-backpropagation in temporally encoded networks of spiking neurons
Sander M Bohte, Joost N Kok, and Han La Poutre · 2002
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The tempotron: a neuron that learns spike timing–based decisions
Robert Gütig and Haim Sompolinsky · 2006
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Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning
Jean-Pascal Pfister, Taro Toyoizumi, David Barber, and Wulfram Gerstner · 2006
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Reinforcement learning through modulation of spike-timing-dependent synaptic plasticity
Răzvan V Florian · 2007
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Solving the distal reward problem through linkage of stdp and dopamine signaling
Eugene M Izhikevich · 2007
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A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback
Robert Legenstein, Dejan Pecevski, and Wolfgang Maass · 2008
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Ermentrout-kopell canonical model
Bard Ermentrout · 2008
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Supervised learning in spiking neural networks with resume: sequence learning, classification, and spike shifting
Filip Ponulak and Andrzej Kasiński · 2010
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Context-dependent computation by recurrent dynamics in prefrontal cortex
Valerio Mante, David Sussillo, Krishna V Shenoy, and William T Newsome · 2013
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Chaos and reliability in balanced spiking networks with temporal drive
Guillaume Lajoie, Kevin K Lin, and Eric Shea-Brown · 2013
Cited alongside, same era.
Predictive coding of dynamical variables in balanced spiking networks
Martin Boerlin, Christian K Machens, and Sophie Denève · 2013
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Performance-optimized hierarchical models predict neural responses in higher visual cortex
Daniel LK Yamins, Ha Hong, Charles F Cadieu, Ethan A Solomon, Darren Seibert, and James J DiCarlo · 2014
Cited alongside, same era.
Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Spiking deep networks with lif neurons
Eric Hunsberger and Chris Eliasmith · 2015
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Neuromodulated spike-timing-dependent plasticity, and theory of three-factor learning rules
Nicolas Frémaux and Wulfram Gerstner · 2015
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Building functional networks of spiking model neurons
LF Abbott, Brian DePasquale, and Raoul-Martin Memmesheimer · 2016
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Efficient codes and balanced networks
Sophie Denève and Christian K Machens · 2016
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Random synaptic feedback weights support error backpropagation for deep learning
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Raoul-Martin Memmesheimer, Ran Rubin, Bence P Ölveczky, and Haim Sompolinsky · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Timothy P Lillicrap, Daniel Cownden, Douglas B Tweed, and Colin J Akerman · 2016
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Learning to represent signals spike by spike
Wieland Brendel, Ralph Bourdoukan, Pietro Vertechi, Christian K Machens, and Sophie Denéve · 2017
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