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Toward a theory of reinforcement-learning connectionist systems
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A learning algorithm for continually running fully recurrent neural networks
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Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
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Weight discretization paradigm for optical neural networks
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Ronald J. Williams · 1992
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Modeling brain function: The world of attractor neural networks
Daniel J Amit · 1992
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A neural substrate of prediction and reward
Wolfram Schultz, Peter Dayan, and P Read Montague · 1997
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type
Guoqiang Bi and Muming Poo · 1998
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Single-photon detection by rod cells of the retina
Foster Rieke and Denis A Baylor · 1998
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Theoretical neuroscience: Computational and mathematical modeling of neural systems
Peter Dayan and Laurence F Abbott · 2001
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A framework for spiking neuron models: The spike response model
Wulfram Gerstner · 2001
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Rate, timing, and cooperativity jointly determine cortical synaptic plasticity
Per Jesper Sjöström, Gina G Turrigiano, and Sacha B Nelson · 2001
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Role of experience and oscillations in transforming a rate code into a temporal code
MR Mehta, AK Lee, and MA Wilson · 2002
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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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Learning in spiking neural networks by reinforcement of stochastic synaptic transmission
Hyunjune Sebastian Seung · 2003
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A 128 × \times 128 pixel 120-db dynamic-range vision-sensor chip for image contrast and orientation extraction
P-F Ruedi, Pascal Heim, François Kaess, Eric Grenet, Friedrich Heitger, P-Y Burgi, Stève Gyger, and Pascal Nussbaum · 2003
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Simple model of spiking neurons
Eugene M Izhikevich · 2003
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The neural basis of the Weber–Fechner law: A logarithmic mental number line
Stanislas Dehaene · 2003
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Robust spatial working memory through homeostatic synaptic scaling in heterogeneous cortical networks
Alfonso Renart, Pengcheng Song, and Xiao-Jing Wang · 2003
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Feedforward, feedback and inhibitory connections in primate visual cortex
Edward M Callaway · 2004
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Extending Spikeprop
Benjamin Schrauwen and Jan Van Campenhout · 2004
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Mean-field theory of irregularly spiking neuronal populations and working memory in recurrent cortical networks
Alfonso Renart, Nicolas Brunel, and Xiao-Jing Wang · 2004
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DNA computing: New computing paradigms
Gheorghe Păun, Grzegorz Rozenberg, and Arto Salomaa · 2005
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Signal propagation and logic gating in networks of integrate-and-fire neurons
Tim P Vogels and Larry F Abbott · 2005
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Learning curves for stochastic gradient descent in linear feedforward networks
Justin Werfel, Xiaohui Xie, and H Sebastian Seung · 2005
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A gradient descent rule for spiking neurons emitting multiple spikes
Olaf Booij and Hieu tat Nguyen · 2005
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A 128 x 128 120db 30mw asynchronous vision sensor that responds to relative intensity change
Patrick Lichtsteiner, Christoph Posch, and Tobi Delbruck · 2006
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A review of the integrate-and-fire neuron model: I. Homogeneous synaptic input
Anthony N Burkitt · 2006
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What is the other 85 percent of V1 doing?
Bruno A Olshausen and David J Field · 2006
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The tempotron: A neuron that learns spike timing–based decisions
Robert Gütig and Haim Sompolinsky · 2006
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Sensory adaptation
Barry Wark, Brian Nils Lundstrom, and Adrienne Fairhall · 2007
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Lapicque’s 1907 paper: From frogs to integrate-and-fire
Nicolas Brunel and Mark CW Van Rossum · 2007
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A solution to the controversy between rate and temporal coding
Shigeru Shinomoto and Shinsuke Koyama · 2007
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Greedy layer-wise training of deep networks
Yoshua Bengio, Pascal Lamblin, Dan Popovici, and Hugo Larochelle · 2007
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Learning real-world stimuli in a neural network with spike-driven synaptic dynamics
Joseph M Brader, Walter Senn, and Stefano Fusi · 2007
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A unified architecture for natural language processing: Deep neural networks with multitask learning
Ronan Collobert and Jason Weston · 2008
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Stochastic properties of coincidence-detector neural cells
Ram Krips and Miriam Furst · 2009
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Recurrent neural network based language model
Tomáš Mikolov, Martin Karafiát, Lukáš Burget, Jan Černockỳ, and Sanjeev Khudanpur · 2010
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{https://www.kaggle.com} , 2010
Kaggle · 2010
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Homeostatic plasticity and STDP: Keeping a neuron’s cool in a fluctuating world
Alanna J Watt and Niraj S Desai · 2010
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Internal representation of task rules by recurrent dynamics: The importance of the diversity of neural responses
Mattia Rigotti, Daniel D Ben Dayan Rubin, Xiao-Jing Wang, and Stefano Fusi · 2010
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Neural network computation with DNA strand displacement cascades
Lulu Qian, Erik Winfree, and Jehoshua Bruck · 2011
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Predictive coding
Yanping Huang and Rajesh PN Rao · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Neural networks for machine learning
Geoffrey Hinton · 2012
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Computing with neural synchrony
Romain Brette · 2012
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Adaptation maintains population homeostasis in primary visual cortex
Andrea Benucci, Aman B Saleem, and Matteo Carandini · 2013
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Mapping from frame-driven to frame-free event-driven vision systems by low-rate rate coding and coincidence processing–application to feedforward ConvNets
José Antonio Pérez-Carrasco, Bo Zhao, Carmen Serrano, Begona Acha, Teresa Serrano-Gotarredona, Shouchun Chen, and Bernabe Linares-Barranco · 2013
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A supervised multi-spike learning algorithm based on gradient descent for spiking neural networks
Yan Xu, Xiaoqin Zeng, Lixin Han, and Jing Yang · 2013
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Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule
Michael Beyeler, Nikil D Dutt, and Jeffrey L Krichmar · 2013
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Immunity to device variations in a spiking neural network with memristive nanodevices
Damien Querlioz, Olivier Bichler, Philippe Dollfus, and Christian Gamrat · 2013
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The CaMKII/NMDAR complex as a molecular memory
Magdalena Sanhueza and John Lisman · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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Towards end-to-end speech recognition with recurrent neural networks
Alex Graves and Navdeep Jaitly · 2014
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A 240 × \times 180 130 db 3 μ \mu s latency global shutter spatiotemporal vision sensor
Christian Brandli, Raphael Berner, Minhao Yang, Shih-Chii Liu, and Tobi Delbruck · 2014
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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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How auto-encoders could provide credit assignment in deep networks via target propagation
Yoshua Bengio · 2014
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Multi-objective optimization
Kalyanmoy Deb · 2014
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Memristive devices for stochastic computing
Siddharth Gaba, Phil Knag, Zhengya Zhang, and Wei Lu · 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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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Learning phrase representations using RNN encoder–decoder for statistical machine translation
Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Feedforward categorization on AER motion events using cortex-like features in a spiking neural network
Bo Zhao, Ruoxi Ding, Shoushun Chen, Bernabe Linares-Barranco, and Huajin Tang · 2014
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A million spiking-neuron integrated circuit with a scalable communication network and interface
Paul A Merolla, John V Arthur, Rodrigo Alvarez-Icaza, Andrew S Cassidy, Jun Sawada, Filipp Akopyan, Bryan L Jackson, Nabil Imam, Chen Guo, Yutaka Nakamura, et al · 2014
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The SpiNNaker project
Steve B Furber, Francesco Galluppi, Steve Temple, and Luis A Plana · 2014
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Fast R-CNN
Ross Girshick · 2015
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Faster R-CNN: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Effective approaches to attention-based neural machine translation
Minh-Thang Luong, Hieu Pham, and Christopher D Manning · 2015
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A neuromorphic event-based neural recording system for smart brain-machine-interfaces
Federico Corradi and Giacomo Indiveri · 2015
Cited alongside, same era.
A scalable population code for time in the striatum
Gustavo BM Mello, Sofia Soares, and Joseph J Paton · 2015
Cited alongside, same era.
Converting static image datasets to spiking neuromorphic datasets using saccades
Garrick Orchard, Ajinkya Jayawant, Gregory K Cohen, and Nitish Thakor · 2015
Cited alongside, same era.
Poker-DVS and MNIST-DVS. Their history, how they were made, and other details
Teresa Serrano-Gotarredona and Bernabe Linares-Barranco · 2015
Cited alongside, same era.
Int. evaluation of an AI system for breast cancer screening
Scott Mayer McKinney, Marcin Sieniek, Varun Godbole, Jonathan Godwin, Natasha Antropova, Hutan Ashrafian, Trevor Back, Mary Chesus, Greg S Corrado, Ara Darzi, et al · 2020
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Hardware implementation of deep network accelerators towards healthcare and biomedical applications
Mostafa Rahimi Azghadi, Corey Lammie, Jason K Eshraghian, Melika Payvand, Elisa Donati, Bernabe Linares-Barranco, and Giacomo Indiveri · 2020
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The computational limits of deep learning
Neil C Thompson, Kristjan Greenewald, Keeheon Lee, and Gabriel F Manso · 2020
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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The carbon impact of artificial intelligence
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Romain Brette · 2015
Cited alongside, same era.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Cited alongside, same era.
Spiking deep networks with LIF neurons
Eric Hunsberger and Chris Eliasmith · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Cited alongside, same era.
DL-ReSuMe: a delay learning-based remote supervised method for spiking neurons
Aboozar Taherkhani, Ammar Belatreche, Yuhua Li, and Liam P Maguire · 2015
Cited alongside, same era.
Unsupervised learning of digit recognition using spike-timing-dependent plasticity
Peter U Diehl and Matthew Cook · 2015
Cited alongside, same era.
Towards biologically plausible deep learning
Yoshua Bengio, Dong-Hyun Lee, Jorg Bornschein, Thomas Mesnard, and Zhouhan Lin · 2015
Cited alongside, same era.
Payal Dhar · 2020
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Carbontracker: Tracking and predicting the carbon footprint of training deep learning models
Lasse F Wolff Anthony, Benjamin Kanding, and Raghavendra Selvan · 2020
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Computation in the human cerebral cortex uses less than 0.2 watts yet this great expense is optimal when considering communication costs
William B Levy and Victoria G Calvert · 2020
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Complementary metal-oxide semiconductor and memristive hardware for neuromorphic computing
Mostafa Rahimi Azghadi, Ying-Chen Chen, Jason K Eshraghian, Jia Chen, Chih-Yang Lin, Amirali Amirsoleimani, Adnan Mehonic, Anthony J Kenyon, Burt Fowler, Jack C Lee, et al · 2020
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A mixed-signal spatio-temporal signal classifier for on-sensor spike sorting
Germain Haessig, Daniel Garcia Lesta, Gregor Lenz, Ryad Benosman, and Piotr Dudek · 2020
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Event-based vision: A survey
Guillermo Gallego, Tobi Delbrück, Garrick Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew J Davison, Jörg Conradt, Kostas Daniilidis, et al · 2020
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Event-based object detection and tracking for space situational awareness
Saeed Afshar, Andrew Peter Nicholson, Andre van Schaik, and Gregory Cohen · 2020
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Spike-timing-dependent back propagation in deep spiking neural networks
Malu Zhang, Jiadong Wang, Zhixuan Zhang, Ammar Belatreche, Jibin Wu, Yansong Chua, Hong Qu, and Haizhou Li · 2020
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Nonlinear retinal response modeling for future neuromorphic instrumentation
Jason K Eshraghian, Seungbum Baek, Timothée Levi, Takashi Kohno, Said Al-Sarawi, Philip HW Leong, Kyoungrok Cho, Derek Abbott, and Omid Kavehei · 2020
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A real-time retinomorphic simulator using a conductance-based discrete neuronal network
Seungbum Baek, Jason K Eshraghian, Wesley Thio, Yulia Sandamirskaya, Herbert HC Iu, and Wei D Lu · 2020
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The Heidelberg spiking data sets for the systematic evaluation of spiking neural networks
Benjamin Cramer, Yannik Stradmann, Johannes Schemmel, and Friedemann Zenke · 2020
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A solution to the learning dilemma for recurrent networks of spiking neurons
Guillaume Bellec, Franz Scherr, Anand Subramoney, Elias Hajek, Darjan Salaj, Robert Legenstein, and Wolfgang Maass · 2020
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Synaptic plasticity dynamics for deep continuous local learning (decolle)
Jacques Kaiser, Hesham Mostafa, and Emre Neftci · 2020
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Temporal backpropagation for spiking neural networks with one spike per neuron
Saeed Reza Kheradpisheh and Timothée Masquelier · 2020
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Backpropagation and the brain
Timothy P Lillicrap, Adam Santoro, Luke Marris, Colin J Akerman, and Geoffrey Hinton · 2020
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Parallel training of deep networks with local updates
Michael Laskin, Luke Metz, Seth Nabarrao, Mark Saroufim, Badreddine Noune, Carlo Luschi, Jascha Sohl-Dickstein, and Pieter Abbeel · 2020
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Power-efficient combinatorial optimization using intrinsic noise in memristor Hopfield neural networks
Fuxi Cai, Suhas Kumar, Thomas Van Vaerenbergh, Xia Sheng, Rui Liu, Can Li, Zhan Liu, Martin Foltin, Shimeng Yu, Qiangfei Xia, et al · 2020
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Evolutionary optimization for neuromorphic systems
Catherine D Schuman, J Parker Mitchell, Robert M Patton, Thomas E Potok, and James S Plank · 2020
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Temporal coding in spiking neural networks with alpha synaptic function
Iulia M Comsa, Krzysztof Potempa, Luca Versari, Thomas Fischbacher, Andrea Gesmundo, and Jyrki Alakuijala · 2020
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A unified framework of online learning algorithms for training recurrent neural networks
Owen Marschall, Kyunghyun Cho, and Cristina Savin · 2020
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Online spatio-temporal learning in deep neural networks
Thomas Bohnstingl, Stanisław Woźniak, Wolfgang Maass, Angeliki Pantazi, and Evangelos Eleftheriou · 2020
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On-chip error-triggered learning of multi-layer memristive spiking neural networks
Melika Payvand, Mohammed E Fouda, Fadi Kurdahi, Ahmed M Eltawil, and Emre O Neftci · 2020
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Brain-score: Which artificial neural network for object recognition is most brain-like?
Martin Schrimpf, Jonas Kubilius, Ha Hong, Najib J Majaj, Rishi Rajalingham, Elias B Issa, Kohitij Kar, Pouya Bashivan, Jonathan Prescott-Roy, Franziska Geiger, et al · 2020
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Hand-gesture recognition based on EMG and event-based camera sensor fusion: A benchmark in neuromorphic computing
Enea Ceolini, Charlotte Frenkel, Sumit Bam Shrestha, Gemma Taverni, Lyes Khacef, Melika Payvand, and Elisa Donati · 2020
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A multimodal AI system for out-of-distribution generalization of seizure detection
Yikai Yang, Nhan Duy Truong, Jason K Eshraghian, Christina Maher, Armin Nikpour, and Omid Kavehei · 2021
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The best of both worlds
Tara Hamilton · 2021
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Energy efficient ecg classification with spiking neural network
Zhanglu Yan, Jun Zhou, and Weng-Fai Wong · 2021
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Neuromorphic control for optic-flow-based landing of mavs using the loihi processor
Julien Dupeyroux, Jesse J Hagenaars, Federico Paredes-Vallés, and Guido CHE de Croon · 2021
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Wind speed forecasting system based on gated recurrent units and convolutional spiking neural networks
Danxiang Wei, Jianzhou Wang, Xinsong Niu, and Zhiwu Li · 2021
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Memory consolidation and improvement by synaptic tagging and capture in recurrent neural networks
Jannik Luboeinski and Christian Tetzlaff · 2021
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Neural heterogeneity promotes robust learning
Nicolas Perez-Nieves, Vincent CH Leung, Pier Luigi Dragotti, and Dan FM Goodman · 2021
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Naturalizing neuromorphic vision event streams using generative adversarial networks
Dennis E Robey, Wesley Thio, Herbert HC Iu, and Jason K Eshraghian · 2021
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Brain-inspired learning on neuromorphic substrates
Friedemann Zenke and Emre O Neftci · 2021
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Prosthesis control using spike rate coding in the retina photoreceptor cells
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Dsec: A stereo event camera dataset for driving scenarios
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Sparse spiking gradient descent
Nicolas Perez-Nieves and Dan FM Goodman · 2021
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The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks
Friedemann Zenke and Tim P Vogels · 2021
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Memristive stochastic computing for deep learning parameter optimization
Corey Lammie, Jason K Eshraghian, Wei D Lu, and Mostafa Rahimi Azghadi · 2021
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Learning without feedback: Fixed random learning signals allow for feedforward training of deep neural networks
Charlotte Frenkel, Martin Lefebvre, and David Bol · 2021
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Multi-objective hyperparameter optimization for spiking neural network neuroevolution
Maryam Parsa, Shruti R Kulkarni, Mark Coletti, Jeffrey Bassett, J Parker Mitchell, and Catherine D Schuman · 2021
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Optimized spiking neurons can classify images with high accuracy through temporal coding with two spikes
Christoph Stöckl and Wolfgang Maass · 2021
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Event-based backpropagation can compute exact gradients for spiking neural networks
Timo C Wunderlich and Christian Pehle · 2021
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Deep residual learning in spiking neural networks
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Revisiting batch normalization for training low-latency deep spiking neural networks from scratch
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Training recurrent neural networks via forward propagation through time
Anil Kag and Venkatesh Saligrama · 2021
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Advancing neuromorphic computing with Loihi: A survey of results and outlook
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An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial eeg
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Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings
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snnTorch
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An implantable neuromorphic sensing system featuring near-sensor computation and send-on-delta transmission for wireless neural sensing of peripheral nerves
Yuming He, Federico Corradi, Chengyao Shi, Stan van der Ven, Martijn Timmermans, Jan Stuijt, Paul Detterer, Pieter Harpe, Lucas Lindeboom, Evelien Hermeling, et al · 2022
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Neuromorphic computing hardware and neural architectures for robotics
Yulia Sandamirskaya, Mohsen Kaboli, Jorg Conradt, and Tansu Celikel · 2022
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Embodied neuromorphic intelligence
Chiara Bartolozzi, Giacomo Indiveri, and Elisa Donati · 2022
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Spiking neural networks for visual place recognition via weighted neuronal assignments
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Spiking neural network for nonlinear regression
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Gooaall!!!: Why we built a neuromorphic robot to play foosball
Gregory Cohen · 2022
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Intelligence processing units accelerate neuromorphic learning
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Exodus: Stable and efficient training of spiking neural networks
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Efficient neuromorphic signal processing with resonator neurons
E Paxon Frady, Sophia Sanborn, Sumit Bam Shrestha, Daniel Ben Dayan Rubin, Garrick Orchard, Friedrich T Sommer, and Mike Davies · 2022
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Error-driven input modulation: solving the credit assignment problem without a backward pass
Giorgia Dellaferrera and Gabriel Kreiman · 2022
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The forward-forward algorithm: Some preliminary investigations
Geoffrey Hinton · 2022
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Weak self-supervised learning for seizure forecasting: a feasibility study
Yikai Yang, Nhan Duy Truong, Jason K Eshraghian, Armin Nikpour, and Omid Kavehei · 2022
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Navigating local minima in quantized spiking neural networks
Jason K Eshraghian, Corey Lammie, Mostafa Rahimi Azghadi, and Wei D Lu · 2022
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Memristor-based binarized spiking neural networks: Challenges and applications
Jason K Eshraghian, Xinxin Wang, and Wei D Lu · 2022
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Xilinx/brevitas, 2022
Alessandro Pappalardo · 2022
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Temporal effective batch normalization in spiking neural networks
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Online spatio-temporal learning in deep neural networks
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Neuromorphic deep spiking neural networks for seizure detection
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Sub-mw neuromorphic snn audio processing applications with rockpool and xylo
Hannah Bos and Dylan Muir · 2023
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Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics
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Spikegpt: Generative pre-trained language model with spiking neural networks
Rui-Jie Zhu, Qihang Zhao, and Jason K Eshraghian · 2023
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Online transformers with spiking neurons for fast prosthetic hand control
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