Fetching the paper…
Reading the bibliography…
A central aim in computational neuroscience is to relate the activity of large populations of neurons to an underlying dynamical system.
Theorie der vielfachen Kontinuität
L Schläfli · 1901
Earlier work this paper cites.
Partition of space
R. C Buck · 1943
Earlier work this paper cites.
Facing up to arrangements: face-count formulas for partitions of space by hyperplanes
T Zaslavsky · 1975
Earlier work this paper cites.
Place units in the hippocampus of the freely moving rat
J O’Keefe · 1976
Earlier work this paper cites.
Bifurcations of recurrent neural networks in gradient descent learning
K Doya · 1993
Earlier work this paper cites.
Phase relationship between hippocampal place units and the eeg theta rhythm
J O’Keefe and M. L Recce · 1993
Earlier work this paper cites.
How the brain keeps the eyes still
H. S Seung · 1996
Earlier work this paper cites.
Physionet: a research resource for studies of complex physiologic and biomedical signals
G Moody, R Mark, and A Goldberger · 2000
Earlier work this paper cites.
Neural Engineering (Computational Neuroscience Series): Computational, Representation, and Dynamics in Neurobiological Systems
C Eliasmith and C. H Anderson · 2002
Earlier work this paper cites.
Bci2000: a general-purpose brain-computer interface (bci) system
G Schalk, D McFarland, T Hinterberger, N Birbaumer, and J Wolpaw · 2004
Earlier work this paper cites.
Rhythms of the Brain
G Buzsáki · 2006
Earlier work this paper cites.
Techniques for extracting single-trial activity patterns from large-scale neural recordings
M. M Churchland, B. M Yu, M Sahani, and K. V Shenoy · 2007
Earlier work this paper cites.
An introduction to hyperplane arrangements
R Stanley · 2007
Earlier work this paper cites.
Application of Girsanov theorem to particle filtering of discretely observed continuous-time non-linear systems
T Sottinen and S Särkkä · 2008
Earlier work this paper cites.
Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity
B. M Yu, J. P Cunningham, G Santhanam, S Ryu, K. V Shenoy, and M Sahani · 2008
Earlier work this paper cites.
Generating coherent patterns of activity from chaotic neural networks
D Sussillo and L. F Abbott · 2009
Earlier work this paper cites.
Factor-analysis methods for higher-performance neural prostheses
G Santhanam, B. M Yu, V Gilja, S. I Ryu, A Afshar, M Sahani, and K. V Shenoy · 2009
Earlier work this paper cites.
Dynamical segmentation of single trials from population neural data
B Petreska, B. M Yu, J. P Cunningham, G Santhanam, S Ryu, K. V Shenoy, and M Sahani · 2011
Earlier work this paper cites.
Empirical models of spiking in neural populations
J. H Macke, L Buesing, J. P Cunningham, B. M Yu, K. V Shenoy, and M Sahani · 2011
Earlier work this paper cites.
A tutorial on particle filtering and smoothing: Fifteen years later
A Doucet and A. M Johansen · 2011
Earlier work this paper cites.
Cortical control of arm movements: A dynamical systems perspective
K. V Shenoy, M Sahani, and M. M Churchland · 2013
Earlier work this paper cites.
Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks
D Sussillo and O Barak · 2013
Earlier work this paper cites.
Dimensionality reduction for large-scale neural recordings
J. P Cunningham and B. M Yu · 2014
Earlier work this paper cites.
Auto-Encoding Variational Bayes
D. P Kingma and M Welling · 2014
Cited alongside, same era.
Discovering governing equations from data by sparse identification of nonlinear dynamical systems
S. L Brunton, J. L Proctor, and J. N Kutz · 2016
Cited alongside, same era.
Uncovering representations of sleep-associated hippocampal ensemble spike activity
Z Chen, A. D Grosmark, H Penagos, and M. A Wilson · 2016
Cited alongside, same era.
Neural manifolds for the control of movement
J Gallego, M Perich, L Miller, and S Solla · 2017
Cited alongside, same era.
A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements
D Durstewitz · 2017
Cited alongside, same era.
Recurrent neural networks as versatile tools of neuroscience research
O Barak · 2017
Neural latents benchmark ’21: Evaluating latent variable models of neural population activity
F Pei, J Ye, D. M Zoltowski, A Wu, R. H Chowdhury, H Sohn, J. E O’Doherty, K. V Shenoy, M. T Kaufman, M Churchland, M Jazayeri, L. E Miller, J Pillow, I. M Park, E. L Dyer, and C Pandarinath · 2021
Later among the works it cites.
Continuous latent process flows
R Deng, M. A Brubaker, G Mori, and A Lehrmann · 2021
Later among the works it cites.
Representation learning for neural population activity with neural data transformers
J Ye and C Pandarinath · 2021
Later among the works it cites.
The role of population structure in computations through neural dynamics
A Dubreuil, A Valente, M Beiran, F Mastrogiuseppe, and S Ostojic · 2022
Later among the works it cites.
Tractable dendritic RNNs for reconstructing nonlinear dynamical systems
M Brenner, F Hess, J. M Mikhaeil, L. F Bereska, Z Monfared, P.-C Kuo, and D Durstewitz · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Bayesian learning and inference in recurrent switching linear dynamical systems
S Linderman, M Johnson, A Miller, R Adams, D Blei, and L Paninski · 2017
Cited alongside, same era.
Filtering variational objectives
C. J Maddison, J Lawson, G Tucker, N Heess, M Norouzi, A Mnih, A Doucet, and Y Teh · 2017
Cited alongside, same era.
Inferring single-trial neural population dynamics using sequential auto-encoders
C Pandarinath, D. J O’Shea, J Collins, R Jozefowicz, S. D Stavisky, J. C Kao, E. M Trautmann, M. T Kaufman, S. I Ryu, L. R Hochberg, J. M Henderson, K. V Shenoy, L. F Abbott, and D Sussillo · 2018
Cited alongside, same era.
Linking connectivity, dynamics, and computations in low-rank recurrent neural networks
F Mastrogiuseppe and S Ostojic · 2018
Cited alongside, same era.
Auto-encoding sequential monte carlo
T. A Le, M Igl, T Rainforth, T Jin, and F Wood · 2018
Cited alongside, same era.
Variational sequential monte carlo
C Naesseth, S Linderman, R Ranganath, and D Blei · 2018
Cited alongside, same era.
T. K Rusch, S Mishra, N. B Erichson, and M. W Mahoney · 2022
Later among the works it cites.
Phase of firing does not reflect temporal order in sequence memory of humans and recurrent neural networks
S Liebe, J Niediek, M Pals, T. P Reber, J Faber, J Bostroem, C. E Elger, J. H Macke, and F Mormann · 2022
Later among the works it cites.
A large-scale neural network training framework for generalized estimation of single-trial population dynamics
M. R Keshtkaran, A. R Sedler, R. H Chowdhury, R Tandon, D Basrai, S. L Nguyen, H Sohn, M Jazayeri, L. E Miller, and C Pandarinath · 2022
Later among the works it cites.
Sixo: Smoothing inference with twisted objectives
D Lawson, A Raventós, A Warrington, and S Linderman · 2022
Later among the works it cites.
Generalized teacher forcing for learning chaotic dynamics
F Hess, Z Monfared, M Brenner, and D Durstewitz · 2023
Later among the works it cites.
CORNN: Convex optimization of recurrent neural networks for rapid inference of neural dynamics
F Dinc, A Shai, M Schnitzer, and H Tanaka · 2023
Later among the works it cites.
Bifurcations and loss jumps in rnn training
L Eisenmann, Z Monfared, N Göring, and D Durstewitz · 2023
Later among the works it cites.
Flow-field inference from neural data using deep recurrent networks
T. D Kim, T. Z Luo, T Can, K Krishnamurthy, J. W Pillow, and C. D Brody · 2023
Later among the works it cites.
Streaming variational monte carlo
Y Zhao, J Nassar, I Jordan, M Bugallo, and I Park · 2023
Later among the works it cites.
Trial matching: capturing variability with data-constrained spiking neural networks
C Sourmpis, C Petersen, W Gerstner, and G Bellec · 2023
Later among the works it cites.
Resampling gradients vanish in differentiable sequential monte carlo samplers
J Zenn and R Bamler · 2023
Later among the works it cites.
Geometry of population activity in spiking networks with low-rank structure
L Cimeša, L Ciric, and S Ostojic · 2023
Later among the works it cites.
Resurrecting recurrent neural networks for long sequences
A Orvieto, S. L Smith, A Gu, A Fernando, C Gulcehre, R Pascanu, and S De · 2023
Later among the works it cites.
Integrating multimodal data for joint generative modeling of complex dynamics
M Brenner, F Hess, G Koppe, and D Durstewitz · 2024
Closest in time.
Trained recurrent neural networks develop phase-locked limit cycles in a working memory task
M Pals, J. H Macke, and O Barak · 2024
Closest in time.
Diversity of emergent dynamics in competitive threshold-linear networks
K Morrison, A Degeratu, V Itskov, and C Curto · 2024
Closest in time.
Expressive dynamics models with nonlinear injective readouts enable reliable recovery of latent features from neural activity
C Versteeg, A. R Sedler, J. D McCart, and C Pandarinath · 2024
Closest in time.