Identifying nonlinear dynamical systems via generative recurrent neural
networks with applications to fMRI

Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI

19 February 2019

Hazem Toutounji

Daniel Durstewitz

Papers citing "Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI"

9 / 9 papers shown

Title
A scalable generative model for dynamical system reconstruction from neuroimaging data Eric Volkmann Alena Brändle Daniel Durstewitz G. Koppe AI4CE 28 1 0 05 Nov 2024
Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data Manuel Brenner Elias Weber G. Koppe Daniel Durstewitz AI4TS AI4CE 39 3 0 07 Oct 2024
When predict can also explain: few-shot prediction to select better neural latents Kabir Dabholkar Omri Barak BDL 55 0 0 23 May 2024
Discovering Causal Relations and Equations from Data Gustau Camps-Valls Andreas Gerhardus Urmi Ninad Gherardo Varando Georg Martius E. Balaguer-Ballester Ricardo Vinuesa Emiliano Díaz L. Zanna Jakob Runge PINN AI4Cl AI4CE CML 35 72 0 21 May 2023
Integrating Multimodal Data for Joint Generative Modeling of Complex Dynamics Manuela Brenner Florian Hess G. Koppe Daniel Durstewitz 28 9 0 15 Dec 2022
Flipped Classroom: Effective Teaching for Time Series Forecasting P. Teutsch Patrick Mäder AI4TS 21 8 0 17 Oct 2022
Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems Manuela Brenner Florian Hess Jonas M. Mikhaeil Leonard Bereska Zahra Monfared Po-Chen Kuo Daniel Durstewitz AI4CE 37 29 0 06 Jul 2022
Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series Daniel Kramer P. Bommer Carlo Tombolini G. Koppe Daniel Durstewitz BDL AI4TS AI4CE 25 18 0 04 Nov 2021
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems Jimmy T.H. Smith Scott W. Linderman David Sussillo 17 28 0 01 Nov 2021