A State Space Approach for Piecewise-Linear Recurrent Neural Networks for Reconstructing Nonlinear Dynamics from Neural Measurements

23 December 2016

Papers citing "A State Space Approach for Piecewise-Linear Recurrent Neural Networks for Reconstructing Nonlinear Dynamics from Neural Measurements"

7 / 7 papers shown

Title
True Zero-Shot Inference of Dynamical Systems Preserving Long-Term Statistics Christoph Jürgen Hemmer Daniel Durstewitz AI4TS SyDa AI4CE 17 0 0 19 May 2025
A scalable generative model for dynamical system reconstruction from neuroimaging data Eric Volkmann Alena Brändle Daniel Durstewitz G. Koppe AI4CE 33 2 0 05 Nov 2024
Inferring stochastic low-rank recurrent neural networks from neural data Matthijs Pals A Erdem Sağtekin Felix Pei Manuel Gloeckler Jakob H Macke 43 7 0 24 Jun 2024
Optimal Recurrent Network Topologies for Dynamical Systems Reconstruction Christoph Jürgen Hemmer Manuel Brenner Florian Hess Daniel Durstewitz 43 4 0 07 Jun 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 51 75 0 21 May 2023
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 42 30 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 27 19 0 04 Nov 2021