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A vast majority of computation in the brain is performed by spiking neural networks.
Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity
Albers, C., Westkott, M., and Pawelzik, K · 1932
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The Chronotron: A Neuron That Learns to Fire Temporally Precise Spike Patterns
Florian, R. V · 1932
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Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding
Gardner, B. and Grüning, A · 1932
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Competitive Learning: From Interactive Activation to Adaptive Resonance
Grossberg, S · 1987
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The recent excitement about neural networks
Crick, F · 1989
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A learning algorithm for continually running fully recurrent neural networks
Williams, R. J. and Zipser, D · 1989
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Different voltage-dependent thresholds for inducing long-term depression and long-term potentiation in slices of rat visual cortex
Artola, A., Bröcher, S., and Singer, W · 1990
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Speed of processing in the human visual system
Thorpe, S., Fize, D., and Marlot, C · 1996
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Metric-space analysis of spike trains: theory, algorithms and application
Victor, J. D. and Purpura, K. P · 1997
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A Novel Spike Distance
van Rossum, M. C. W · 2001
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Error-backpropagation in temporally encoded networks of spiking neurons
Bohte, S. M., Kok, J. N., and La Poutre, H · 2002
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Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations
Maass, W., Natschläger, T., and Markram, H · 2002
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A gradient descent rule for spiking neurons emitting multiple spikes
Booij, O. and tat Nguyen, H · 2005
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Spike-timing dependent plasticity and mutual information maximization for a spiking neuron model
Toyoizumi, T., Pfister, J.-p., Aihara, K., and Gerstner, W · 2005
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The tempotron: a neuron that learns spike timing-based decisions
Gütig, R. and Sompolinsky, H · 2006
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Fast Modifications of the SpikeProp Algorithm
McKennoch, S., Liu, D., and Bushnell, L. G · 2006
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Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning
Pfister, J.-P., Toyoizumi, T., Barber, D., and Gerstner, W · 2006
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A learning rule for very simple universal approximators consisting of a single layer of perceptrons
Auer, P., Burgsteiner, H., and Maass, W · 2007
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Reducing the Variability of Neural Responses: A Computational Theory of Spike-Timing-Dependent Plasticity
Bohte, S. M. and Mozer, M. C · 2007
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Solving the Distal Reward Problem through Linkage of STDP and Dopamine Signaling
Izhikevich, E. M · 2007
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Rapid Neural Coding in the Retina with Relative Spike Latencies
Gollisch, T. and Meister, M · 2008
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A Gradient Learning Rule for the Tempotron
Urbanczik, R. and Senn, W · 2008
Earlier work this paper cites.
Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting
Ponulak, F. and Kasiński, A · 2009
Earlier work this paper cites.
Functional Requirements for Reward-Modulated Spike-Timing-Dependent Plasticity
Fremaux, N., Sprekeler, H., and Gerstner, W · 2010
Cited alongside, same era.
Timing is not Everything: Neuromodulation Opens the STDP Gate
Pawlak, V., Wickens, J. R., Kirkwood, A., and Kerr, J. N. D · 2010
Cited alongside, same era.
Error-Backpropagation in Networks of Fractionally Predictive Spiking Neurons
Bohte, S. M · 2011
Cited alongside, same era.
A Large-Scale Model of the Functioning Brain
Eliasmith, C., Stewart, T. C., Choo, X., Bekolay, T., DeWolf, T., Tang, Y., and Rasmussen, D · 2012
Cited alongside, same era.
The Spike-Timing Dependence of Plasticity
Feldman, D · 2012
Cited alongside, same era.
Neural networks for machine learning. Coursera, video lectures
Hinton, G · 2012
Cited alongside, same era.
Learning universal computations with spikes
Thalmeier, D., Uhlmann, M., Kappen, H. J., and Memmesheimer, R.-M · 2015
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Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks
Zenke, F., Agnes, E. J., and Gerstner, W · 2015
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Building functional networks of spiking model neurons
Abbott, L. F., DePasquale, B., and Memmesheimer, R.-M · 2016
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Learning in the Machine: Random Backpropagation and the Learning Channel
Baldi, P., Sadowski, P., and Lu, Z · 2016
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Learning Precise Spike Train–to–Spike Train Transformations in Multilayer Feedforward Neuronal Networks
Banerjee, A · 2016
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On the Analytical Solution of Firing Time for SpikeProp
de Montigny, S. and Mâsse, B. R · 2016
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Span: spike pattern association neuron for learning spatio-temporal spike patterns
Mohemmed, A., Schliebs, S., Matsuda, S., and Kasabov, N · 2012
Cited alongside, same era.
Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
Bengio, Y., Léonard, N., and Courville, A · 2013
Cited alongside, same era.
Matching Recall and Storage in Sequence Learning with Spiking Neural Networks
Brea, J., Senn, W., and Pfister, J.-P · 2013
Cited alongside, same era.
Supervised Learning in Multilayer Spiking Neural Networks
Sporea, I. and Grüning, A · 2013
Cited alongside, same era.
Neuronal dynamics: from single neurons to networks and models of cognition
Gerstner, W., Kistler, W. M., Naud, R., and Paninski, L · 2014
Cited alongside, same era.
Stochastic variational learning in recurrent spiking networks
Jimenez Rezende, D. and Gerstner, W · 2014
Cited alongside, same era.
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Efficient codes and balanced networks
Denève, S. and Machens, C. K · 2016
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Biologically feasible deep learning with segregated dendrites
Guergiuev, J., Lillicrap, T. P., and Richards, B. A · 2016
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Spiking neurons can discover predictive features by aggregate-label learning
Gütig, R · 2016
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Random synaptic feedback weights support error backpropagation for deep learning
Lillicrap, T. P., Cownden, D., Tweed, D. B., and Akerman, C. J · 2016
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Toward an Integration of Deep Learning and Neuroscience
Marblestone, A. H., Wayne, G., and Kording, K. P · 2016
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Representational Distance Learning for Deep Neural Networks
McClure, P. and Kriegeskorte, N · 2016
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Deep Learning Models of the Retinal Response to Natural Scenes
McIntosh, L., Maheswaranathan, N., Nayebi, A., Ganguli, S., and Baccus, S · 2016
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Towards deep learning with spiking neurons in energy based models with contrastive Hebbian plasticity
Mesnard, T., Gerstner, W., and Brea, J · 2016
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Neuromorphic Deep Learning Machines
Neftci, E., Augustine, C., Paul, S., and Detorakis, G · 2016
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Robust learning in SpikeProp
Shrestha, S. B. and Song, Q · 2016
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Learning to represent signals spike by spike
Brendel, W., Bourdoukan, R., Vertechi, P., Machens, C. K., and Denéve, S · 2017
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Predicting non-linear dynamics: a stable local learning scheme for recurrent spiking neural networks
Gilra, A. and Gerstner, W · 2017
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Gradient Descent for Spiking Neural Networks
Huh, D. and Sejnowski, T. J · 2017
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Learning with three factors: modulating Hebbian plasticity with errors
Kusmierz, L., Isomura, T., and Toyoizumi, T · 2017
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Pattern representation and recognition with accelerated analog neuromorphic systems
Petrovici, M. A., Schmitt, S., Klähn, J., Stöckel, D., Schroeder, A., Bellec, G., Bill, J., Breitwieser, O., Bytschok, I., Grübl, A., Güttler, M., Hartel, A., Hartmann, S., Husmann, D., Husmann, K., Jeltsch, S., Karasenko, V., Kleider, M., Koke, C., Kononov, A., Mauch, C., Müller, P., Partzsch, J., Pfeil, T., Schiefer, S., Scholze, S., Subramoney, A., Thanasoulis, V., Vogginger, B., Legenstein, R., Maass, W., Schüffny, R., Mayr, C., Schemmel, J., and Meier, K · 2017
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ssbm: SuperSpike benchmark suite
Zenke, F · 2017
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