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A growing body of work underlines striking similarities between biological neural networks and recurrent, binary neural networks.
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Gradient-based learning applied to document recognition
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Spikeprop: backpropagation for networks of spiking neurons
Sander M Bohte, Joost N Kok, and Johannes A La Poutré · 2000
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The “echo state” approach to analysing and training recurrent neural networks-with an erratum note
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Spiking Neuron Models. Single Neurons, Populations, Plasticity
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Real-time computing without stable states: A new framework for neural computation based on perturbations
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Neural engineering: Computation, representation, and dynamics in neurobiological systems
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Silicon synaptic homeostasis
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The tempotron: a neuron that learns spike timing–based decisions
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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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Simulation of networks of spiking neurons: A review of tools and strategies
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An 128x128 120dB 15 μ \mu s-latency temporal contrast vision sensor
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Generating coherent patterns of activity from chaotic neural networks
David Sussillo and Larry F Abbott · 2009
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Connectivity reflects coding: a model of voltage-based stdp with homeostasis
C. Clopath, L. Büsing, E. Vasilaki, and W. Gerstner · 2010
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A systematic method for configuring VLSI networks of spiking neurons
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Neuromorphic electronic circuits for building autonomous cognitive systems
E. Chicca, F. Stefanini, and G. Indiveri · 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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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Random feedback weights support learning in deep neural networks
Timothy P Lillicrap, Daniel Cownden, Douglas B Tweed, and Colin J Akerman · 2014
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A million spiking-neuron integrated circuit with a scalable communication network and interface
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Learning in the machine: The symmetries of the deep learning channel
Pierre Baldi, Peter Sadowski, and Zhiqin Lu · 2017
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Automatic differentiation in machine learning: a survey
Atılım Günes Baydin, Barak A Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind · 2017
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Gradient descent for spiking neural networks
Dongsung Huh and Terrence J Sejnowski · 2017
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Deep supervised learning using local errors
Hesham Mostafa, Vishwajith Ramesh, and Gert Cauwenberghs · 2017
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Event-driven random back-propagation: Enabling neuromorphic deep learning machines
EO Neftci, C Augustine, S Paul, and Georgios Detorakis · 2017
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Striving for simplicity: The all convolutional net
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Learning by the dendritic prediction of somatic spiking
Robert Urbanczik and Walter Senn · 2014
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Converting static image datasets to spiking neuromorphic datasets using saccades
Garrick Orchard, Ajinkya Jayawant, Gregory K. Cohen, and Nitish Thakor · 2015
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Learning spatiotemporal features with 3d convolutional networks
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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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Friedemann Zenke and Surya Ganguli · 2017
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Assessing the scalability of biologically-motivated deep learning algorithms and architectures
Sergey Bartunov, Adam Santoro, Blake Richards, Luke Marris, Geoffrey E Hinton, and Timothy Lillicrap · 2018
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Long short-term memory and learning-to-learn in networks of spiking neurons
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Loihi: A neuromorphic manycore processor with on-chip learning
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Synaptic plasticity for deep continuous local learning
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Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks
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Error-triggered three-factor learning dynamics for crossbar arrays
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