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Expressive architectures enhance interpretability of dynamics-based
  neural population models

Expressive architectures enhance interpretability of dynamics-based neural population models

7 December 2022
Andrew R. Sedler
Chris VerSteeg
C. Pandarinath
ArXivPDFHTML

Papers citing "Expressive architectures enhance interpretability of dynamics-based neural population models"

14 / 14 papers shown
Title
When predict can also explain: few-shot prediction to select better neural latents
When predict can also explain: few-shot prediction to select better neural latents
Kabir V. Dabholkar
Omri Barak
BDL
83
0
0
23 May 2024
Learnable latent embeddings for joint behavioral and neural analysis
Learnable latent embeddings for joint behavioral and neural analysis
Steffen Schneider
Jin Hwa Lee
Mackenzie W. Mathis
54
217
0
01 Apr 2022
Discovering Governing Equations from Partial Measurements with Deep
  Delay Autoencoders
Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders
Joseph Bakarji
Kathleen P. Champion
J. Nathan Kutz
Steven L. Brunton
78
84
0
13 Jan 2022
Reverse engineering recurrent neural networks with Jacobian switching
  linear dynamical systems
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems
Jimmy T.H. Smith
Scott W. Linderman
David Sussillo
70
28
0
01 Nov 2021
Identifiable Deep Generative Models via Sparse Decoding
Identifiable Deep Generative Models via Sparse Decoding
Gemma E. Moran
Dhanya Sridhar
Yixin Wang
David M. Blei
BDL
50
46
0
20 Oct 2021
Chaos as an interpretable benchmark for forecasting and data-driven
  modelling
Chaos as an interpretable benchmark for forecasting and data-driven modelling
W. Gilpin
AI4TS
42
80
0
11 Oct 2021
Neural Latents Benchmark '21: Evaluating latent variable models of
  neural population activity
Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity
Felix Pei
Joel Ye
D. Zoltowski
Anqi Wu
Raeed H. Chowdhury
...
L. Miller
Jonathan W. Pillow
Il Memming Park
Eva L. Dyer
C. Pandarinath
171
87
0
09 Sep 2021
Building population models for large-scale neural recordings:
  opportunities and pitfalls
Building population models for large-scale neural recordings: opportunities and pitfalls
C. Hurwitz
N. Kudryashova
A. Onken
Matthias H Hennig
32
39
0
03 Feb 2021
PyTorch: An Imperative Style, High-Performance Deep Learning Library
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke
Sam Gross
Francisco Massa
Adam Lerer
James Bradbury
...
Sasank Chilamkurthy
Benoit Steiner
Lu Fang
Junjie Bai
Soumith Chintala
ODL
302
42,038
0
03 Dec 2019
Universality and individuality in neural dynamics across large
  populations of recurrent networks
Universality and individuality in neural dynamics across large populations of recurrent networks
Niru Maheswaranathan
Alex H. Williams
Matthew D. Golub
Surya Ganguli
David Sussillo
56
143
0
19 Jul 2019
Reverse engineering recurrent networks for sentiment classification
  reveals line attractor dynamics
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics
Niru Maheswaranathan
Alex H. Williams
Matthew D. Golub
Surya Ganguli
David Sussillo
45
78
0
25 Jun 2019
Tune: A Research Platform for Distributed Model Selection and Training
Tune: A Research Platform for Distributed Model Selection and Training
Richard Liaw
Eric Liang
Robert Nishihara
Philipp Moritz
Joseph E. Gonzalez
Ion Stoica
149
887
0
13 Jul 2018
LFADS - Latent Factor Analysis via Dynamical Systems
LFADS - Latent Factor Analysis via Dynamical Systems
David Sussillo
Rafal Jozefowicz
L. F. Abbott
C. Pandarinath
AI4CE
43
90
0
22 Aug 2016
Linear dynamical neural population models through nonlinear embeddings
Linear dynamical neural population models through nonlinear embeddings
Yuanjun Gao
Evan Archer
Liam Paninski
John P. Cunningham
57
155
0
26 May 2016
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