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Random Recurrent Neural Networks (RRNN) are the simplest recurrent networks to model and extract features from sequential data.
Rank-one modification of the symmetric eigenproblem,
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Temporal information transformed into a spatial code by a neural network with realistic properties,
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Long short-term memory,
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The variable discharge of cortical neurons: implications for connectivity, computation, and information coding,
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The “echo state” approach to analysing and training recurrent neural networks-with an erratum note,
H. Jaeger, · 2001
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Learning the long-term structure of the blues,
D. Eck, J. Schmidhuber, · 2002
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Real-time computing without stable states: A new framework for neural computation based on perturbations,
W. Maass, T. Natschläger, H. Markram, · 2002
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Adaptive nonlinear system identification with echo state networks,
H. Jaeger, · 2003
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Simple model of spiking neurons,
E. M. Izhikevich, · 2003
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Neocortical network activity in vivo is generated through a dynamic balance of excitation and inhibition,
B. Haider, A. Duque, A. R. Hasenstaub, D. A. McCormick, · 2006
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Ermentrout-kopell canonical model,
B. Ermentrout, · 2008
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Generating coherent patterns of activity from chaotic neural networks,
D. Sussillo, L. F. Abbott, · 2009
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Sequential deep learning for human action recognition,
M. Baccouche, F. Mamalet, C. Wolf, C. Garcia, A. Baskurt, · 2011
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O. Sporns, Discovering the human connectome, MIT press, 2012
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Speech recognition with deep recurrent neural networks,
A. Graves, A.-r. Mohamed, G. Hinton, · 2013
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On the difficulty of training recurrent neural networks,
R. Pascanu, T. Mikolov, Y. Bengio, · 2013
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Generating sequences with recurrent neural networks,
A. Graves, · 2013
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Spoken language understanding using long short-term memory neural networks,
K. Yao, B. Peng, Y. Zhang, D. Yu, G. Zweig, Y. Shi, · 2014
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Learning phrase representations using rnn encoder-decoder for statistical machine translation,
Orthogonal recurrent neural networks with scaled cayley transform,
K. Helfrich, D. Willmott, Q. Ye, · 2017
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On orthogonality and learning recurrent networks with long term dependencies,
E. Vorontsov, C. Trabelsi, S. Kadoury, C. Pal, · 2017
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Stable architectures for deep neural networks,
E. Haber, L. Ruthotto, · 2017
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Supervised learning in spiking neural networks with force training,
W. Nicola, C. Clopath, · 2017
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Balanced excitation and inhibition are required for high-capacity, noise-robust neuronal selectivity,
R. Rubin, L. Abbott, H. Sompolinsky, · 2017
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Audio to body dynamics,
E. Shlizerman, L. Dery, H. Schoen, I. Kemelmacher-Shlizerman, · 2018
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K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, Y. Bengio, · 2014
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Learning longer memory in recurrent neural networks,
T. Mikolov, A. Joulin, S. Chopra, M. Mathieu, M. Ranzato, · 2014
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Neural population spiking activity during singing: adult and longitudinal developmental recordings in the zebra finch,
S. Crandall, T. Nick, · 2014
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A simple way to initialize recurrent networks of rectified linear units,
Q. V. Le, N. Jaitly, G. E. Hinton, · 2015
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Train faster, generalize better: Stability of stochastic gradient descent,
M. Hardt, B. Recht, Y. Singer, · 2016
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Full-capacity unitary recurrent neural networks,
S. Wisdom, T. Powers, J. Hershey, J. Le Roux, L. Atlas, · 2016
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Unitary evolution recurrent neural networks,
M. Arjovsky, A. Shah, Y. Bengio, · 2016
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full-force: A target-based method for training recurrent networks,
B. DePasquale, C. J. Cueva, K. Rajan, L. Abbott, et al., · 2018
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A data set of human body movements for physical rehabilitation exercises,
A. Vakanski, H.-p. Jun, D. Paul, R. Baker, · 2018
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Complex unitary recurrent neural networks using scaled cayley transform,
K. D. Maduranga, K. E. Helfrich, Q. Ye, · 2019
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Antisymmetricrnn: A dynamical system view on recurrent neural networks,
B. Chang, M. Chen, E. Haber, E. H. Chi, · 2019
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Whole-animal connectomes of both caenorhabditis elegans sexes,
S. J. Cook, T. A. Jarrell, C. A. Brittin, Y. Wang, A. E. Bloniarz, M. A. Yakovlev, K. C. Nguyen, L. T.-H. Tang, E. A. Bayer, J. S. Duerr, et al., · 2019
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Clustering and recognition of spatiotemporal features through interpretable embedding of sequence to sequence recurrent neural networks,
K. Su, E. Shlizerman, · 2020
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Complete connectomic reconstruction of olfactory projection neurons in the fly brain,
A. S. Bates, P. Schlegel, R. J. Roberts, N. Drummond, I. F. Tamimi, R. G. Turnbull, X. Zhao, E. C. Marin, P. D. Popovici, S. Dhawan, et al., · 2020
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Transfer-rls method and transfer-force learning for simple and fast training of reservoir computing models,
H. Tamura, G. Tanaka, · 2021
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