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The recent discovered spatial-temporal information processing capability of bio-inspired Spiking neural networks (SNN) has enabled some interesting models and applications.
Fir and iir synapses, a new neural network architecture for time series modeling
Andrew D Back and Ah Chung Tsoi · 1991
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When is an integrate-and-fire neuron like a poisson neuron?
Charles F Stevens and Anthony M Zador · 1996
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On-line learning algorithms for locally recurrent neural networks
Paolo Campolucci, Aurelio Uncini, Francesco Piazza, and Bhaskar D Rao · 1999
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Temporal classification: Extending the classification paradigm to multivariate time series
Mohammed Waleed Kadous et al · 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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Simulation of networks of spiking neurons: a review of tools and strategies
Romain Brette, Michelle Rudolph, Ted Carnevale, Michael Hines, David Beeman, James M Bower, Markus Diesmann, Abigail Morrison, Philip H Goodman, Frederick C Harris, et al · 2007
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Speaker-independent isolated digit recognition using an aer silicon cochlea
Mohammad Abdollahi and Shih-Chii Liu · 2011
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Span: Spike pattern association neuron for learning spatio-temporal spike patterns
Ammar Mohemmed, Stefan Schliebs, Satoshi Matsuda, and Nikola Kasabov · 2012
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Theoretical models of synaptic short term plasticity
Matthias H Hennig · 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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Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
Peter U Diehl, Daniel Neil, Jonathan Binas, Matthew Cook, Shih-Chii Liu, and Michael Pfeiffer · 2015
Cited alongside, same era.
Backpropagation for energy-efficient neuromorphic computing
Steve K Esser, Rathinakumar Appuswamy, Paul Merolla, John V Arthur, and Dharmendra S Modha · 2015
Cited alongside, same era.
Spiking neurons can discover predictive features by aggregate-label learning
Robert Gütig · 2016
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Training deep spiking neural networks using backpropagation
Jun Haeng Lee, Tobi Delbruck, and Michael Pfeiffer · 2016
Cited alongside, same era.
A low power, fully event-based gesture recognition system
Arnon Amir, Brian Taba, David Berg, Timothy Melano, Jeffrey McKinstry, Carmelo Di Nolfo, Tapan Nayak, Alexander Andreopoulos, Guillaume Garreau, Marcela Mendoza, et al · 2017
Cited alongside, same era.
Superspike: Supervised learning in multilayer spiking neural networks
Friedemann Zenke and Surya Ganguli · 2018
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Stca: spatio-temporal credit assignment with delayed feedback in deep spiking neural networks
Pengjie Gu, Rong Xiao, Gang Pan, and Huajin Tang · 2019
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Embodied event-driven random backpropagation
Jacques Kaiser, Alexander Friedrich, J Tieck, Daniel Reichard, Arne Roennau, Emre Neftci, and Rüdiger Dillmann · 2019
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Multivariate lstm-fcns for time series classification
Fazle Karim, Somshubra Majumdar, Houshang Darabi, and Samuel Harford · 2019
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Surrogate gradient learning in spiking neural networks
Emre O Neftci, Hesham Mostafa, and Friedemann Zenke · 2019
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Yingyezhe Jin, Wenrui Zhang, and Peng Li · 2018
Cited alongside, same era.
Synaptic plasticity dynamics for deep continuous local learning
Jacques Kaiser, Hesham Mostafa, and Emre Neftci · 2018
Cited alongside, same era.
Slayer: Spike layer error reassignment in time
Sumit Bam Shrestha and Garrick Orchard · 2018
Cited alongside, same era.
A biologically plausible speech recognition framework based on spiking neural networks
Jibin Wu, Yansong Chua, and Haizhou Li · 2018
Cited alongside, same era.
Spatio-temporal backpropagation for training high-performance spiking neural networks
Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, and Luping Shi · 2018
Cited alongside, same era.
Neural population coding for effective temporal classification
Zihan Pan, Jibin Wu, Malu Zhang, Haizhou Li, and Yansong Chua · 2019
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Approximating back-propagation for a biologically plausible local learning rule in spiking neural networks
Amar Shrestha, Haowen Fang, Qing Wu, and Qinru Qiu · 2019
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Bp-stdp: Approximating backpropagation using spike timing dependent plasticity
Amirhossein Tavanaei and Anthony Maida · 2019
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Direct training for spiking neural networks: Faster, larger, better
Yujie Wu, Lei Deng, Guoqi Li, Jun Zhu, Yuan Xie, and Luping Shi · 2019
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