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Understanding how the dynamics in biological and artificial neural networks implement the computations required for a task is a salient open question in machine learning and neuroscience.
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S. and Pitts, W · 1943
Earlier work this paper cites.
Applications of centre manifold theory , volume 35
Carr, J · 1981
Earlier work this paper cites.
Random networks of automata: a simple annealed approximation
Derrida, B. and Pomeau, Y · 1986
Earlier work this paper cites.
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., and Williams, R. J · 1986
Earlier work this paper cites.
Temporal association in asymmetric neural networks
Sompolinsky, H. and Kanter, I · 1986
Earlier work this paper cites.
The space of interactions in neural network models
Gardner, E · 1988
Earlier work this paper cites.
Neural networks that learn temporal sequences
Nadal, J.-P · 1988
Earlier work this paper cites.
Chaos in random neural networks
Sompolinsky, H., Crisanti, A., and Sommers, H. J · 1988
Earlier work this paper cites.
Finding structure in time
Elman, J. L · 1990
Earlier work this paper cites.
On the storage capacity for temporal pattern sequences in networks with delays
Bauer, K. and Krey, U · 1991
Earlier work this paper cites.
Neural network capacity for temporal sequence storage
Taylor, J. G · 1991
Earlier work this paper cites.
Temporal sequence storage capacity of time-summating neural networks
Bressloff, P. and Taylor, J · 1992
Earlier work this paper cites.
Computing with infinite networks
Williams, C · 1996
Earlier work this paper cites.
Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
Earlier work this paper cites.
Statistical mechanics of learning
Engel, A. and Van den Broeck, C · 2001
Earlier work this paper cites.
Neural network dynamics
Vogels, T. P., Rajan, K., and Abbott, L · 2005
Earlier work this paper cites.
Estimation of parameters in nonlinear systems using balanced synchronization
Abarbanel, H. D. I., Creveling, D. R., and Jeanne, J. M · 2008
Earlier work this paper cites.
Generating coherent patterns of activity from chaotic neural networks
Sussillo, D. and Abbott, L · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
Glorot, X. and Bengio, Y · 2010
Earlier work this paper cites.
Neural population dynamics during reaching
Churchland, M. M., Cunningham, J. P., Kaufman, M. T., Foster, J. D., Nuyujukian, P., Ryu, S. I., and Shenoy, K. V · 2012
Earlier work this paper cites.
Choice-specific sequences in parietal cortex during a virtual-navigation decision task
Harvey, C. D., Coen, P., and Tank, D. W · 2012
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Todorov, E., Erez, T., and Tassa, Y · 2012
Earlier work this paper cites.
Balanced cortical microcircuitry for maintaining information in working memory
Lim, S. and Goldman, M. S · 2013
Earlier work this paper cites.
Context-dependent computation by recurrent dynamics in prefrontal cortex
Mante, V., Sussillo, D., Shenoy, K. V., and Newsome, W. T · 2013
Earlier work this paper cites.
On the difficulty of training recurrent neural networks
Pascanu, R., Mikolov, T., and Bengio, Y · 2013
Earlier work this paper cites.
Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks
Sussillo, D. and Barak, O · 2013
Earlier work this paper cites.
Learning phrase representations using rnn encoder-decoder for statistical machine translation, 2014
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y · 2014
Earlier work this paper cites.
Neural turing machines
Graves, A., Wayne, G., and Danihelka, I · 2014
Earlier work this paper cites.
Cortical activity in the null space: permitting preparation without movement
Kaufman, M. T., Churchland, M. M., Ryu, S. I., and Shenoy, K. V · 2014
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2015
Cited alongside, same era.
An empirical exploration of recurrent network architectures
Jozefowicz, R., Zaremba, W., and Sutskever, I · 2015
Cited alongside, same era.
Is cortical connectivity optimized for storing information?
Brunel, N · 2016
Cited alongside, same era.
Computational principles of memory
Chaudhuri, R. and Fiete, I · 2016
Cited alongside, same era.
Training excitatory-inhibitory recurrent neural networks for cognitive tasks: A simple and flexible framework
Song, H. F., Yang, G. R., and Wang, X.-J · 2016
Cited alongside, same era.
Julia: A fresh approach to numerical computing
Bezanson, J., Edelman, A., Karpinski, S., and Shah, V. B · 2017
Cited alongside, same era.
How to train your neural ODE: the world of Jacobian and kinetic regularization
Finlay, C., Jacobsen, J.-H., Nurbekyan, L., and Oberman, A · 2020
Later among the works it cites.
STEER : Simple Temporal Regularization for Neural ODE
Ghosh, A., Behl, H., Dupont, E., Torr, P., and Namboodiri, V · 2020
Later among the works it cites.
Statistical field theory for neural networks
Helias, M. and Dahmen, D · 2020
Later among the works it cites.
Learning differential equations that are easy to solve
Kelly, J., Bettencourt, J., Johnson, M. J., and Duvenaud, D · 2020
Later among the works it cites.
Neural Controlled Differential Equations for Irregular Time Series
Kidger, P., Morrill, J., Foster, J., and Lyons, T · 2020
Later among the works it cites.
Learning Long-Term dependencies in Irregularly-Sampled time series
Lechner, M. and Hasani, R · 2020
Later among the works it cites.
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Capacity and trainability in recurrent neural networks
Collins, J., Sohl-Dickstein, J., and Sussillo, D · 2017
Cited alongside, same era.
Tips for training recurrent neural networks
Hafner, D · 2017
Cited alongside, same era.
Deep neural networks as gaussian processes
Lee, J., Bahri, Y., Novak, R., Schoenholz, S. S., Pennington, J., and Sohl-Dickstein, J · 2017
Cited alongside, same era.
Differentialequations.jl–a performant and feature-rich ecosystem for solving differential equations in julia
Rackauckas, C. and Nie, Q · 2017
Cited alongside, same era.
Deep information propagation
Schoenholz, S. S., Gilmer, J., Ganguli, S., and Sohl-Dickstein, J · 2017
Cited alongside, same era.
The uea multivariate time series classification archive, 2018
Bagnall, A., Dau, H. A., Lines, J., Flynn, M., Large, J., Bostrom, A., Southam, P., and Keogh, E · 2018
Cited alongside, same era.
Scalable gradients for stochastic differential equations
Li, X., Wong, T.-K. L., Chen, R. T. Q., and Duvenaud, D · 2020
Later among the works it cites.
Discretize-optimize vs. optimize-discretize for time-series regression and continuous normalizing flows
Onken, D. and Ruthotto, L · 2020
Later among the works it cites.
Universal differential equations for scientific machine learning
Rackauckas, C., Ma, Y., Martensen, J., Warner, C., Zubov, K., Supekar, R., Skinner, D., and Ramadhan, A · 2020
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Rusch, T. K. and Mishra, S · 2020
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Computation through neural population dynamics
Vyas, S., Golub, M. D., Sussillo, D., and Shenoy, K. V · 2020
Later among the works it cites.
The geometry of integration in text classification rnns
Aitken, K., Ramasesh, V. V., Garg, A., Cao, Y., Sussillo, D., and Maheswaranathan, N · 2021
Later among the works it cites.
Emergence of memory manifolds
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Later among the works it cites.
Doshi, D., He, T., and Gromov, A · 2021
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Gated recurrent units viewed through the lens of continuous time dynamical systems
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Later among the works it cites.
Inferring latent dynamics underlying neural population activity via neural differential equations
Kim, T. D., Luo, T. Z., Pillow, J. W., and Brody, C. D · 2021
Later among the works it cites.
Neural controlled differential equations for online prediction tasks
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Geometry of abstract learned knowledge in the hippocampus
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Later among the works it cites.
Opening the blackbox: Accelerating neural differential equations by regularizing internal solver heuristics
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Long expressive memory for sequence modeling
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Heavy Ball Neural Ordinary Differential Equations
Xia, H., Suliafu, V., Ji, H., Nguyen, T. M., Bertozzi, A., Osher, S., and Wang, B · 2021
Later among the works it cites.
Flexible multitask computation in recurrent networks utilizes shared dynamical motifs
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Efficiently modeling long sequences with structured state spaces, 2022
Gu, A., Goel, K., and Ré, C · 2022
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Attractor and integrator networks in the brain
Khona, M. and Fiete, I. R · 2022
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On Neural Differential Equations
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Theory of gating in recurrent neural networks
Krishnamurthy, K., Can, T., and Schwab, D. J · 2022
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Wide and deep neural networks achieve optimality for classification
Radhakrishnan, A., Belkin, M., and Uhler, C · 2022
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Expressive architectures enhance interpretability of dynamics-based neural population models, 2023
Sedler, A. R., Versteeg, C., and Pandarinath, C · 2023
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