Daniel Durstewitz

According to our database, Daniel Durstewitz authored at least 38 papers between 1996 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

What Neuroscience Can Teach AI About Learning in Continuously Changing Environments.

Daniel Durstewitz, Bruno Averbeck, Georgia Koppe

CoRR, July, 2025

True Zero-Shot Inference of Dynamical Systems Preserving Long-Term Statistics.

Christoph Jürgen Hemmer, Daniel Durstewitz

CoRR, May, 2025

Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data.

Manuel Brenner, Elias Weber, Georgia Koppe, Daniel Durstewitz

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Visualization of Discontinuous Vector Field Topology.

Egzon Miftari, Daniel Durstewitz, Filip Sadlo

IEEE Trans. Vis. Comput. Graph., January, 2024

dsLassoCov: a federated machine learning approach incorporating covariate control.

Han Cao, Augusto Anguita, Charline Warembourg, Xavier Escriba-Montagut, Martine Vrijheid, Juan R. González, Tim Cadman, Verena Schneider-Lindner, Daniel Durstewitz, Xavier Basagaña, Emanuel Schwarz

CoRR, 2024

MTLComb: multi-task learning combining regression and classification tasks for joint feature selection.

Han Cao, Sivanesan Rajan, Bianka Hahn, Ersoy Kocak, Daniel Durstewitz, Emanuel Schwarz, Verena Schneider-Lindner

CoRR, 2024

A scalable generative model for dynamical system reconstruction from neuroimaging data.

Eric Volkmann, Alena Brändle, Daniel Durstewitz, Georgia Koppe

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Almost-Linear RNNs Yield Highly Interpretable Symbolic Codes in Dynamical Systems Reconstruction.

Manuel Brenner, Christoph Jürgen Hemmer, Zahra Monfared, Daniel Durstewitz

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Optimal Recurrent Network Topologies for Dynamical Systems Reconstruction.

Christoph Jürgen Hemmer, Manuel Brenner, Florian Hess, Daniel Durstewitz

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Out-of-Domain Generalization in Dynamical Systems Reconstruction.

Niclas Alexander Göring, Florian Hess, Manuel Brenner, Zahra Monfared, Daniel Durstewitz

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Integrating Multimodal Data for Joint Generative Modeling of Complex Dynamics.

Manuel Brenner, Florian Hess, Georgia Koppe, Daniel Durstewitz

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Bifurcations and loss jumps in RNN training.

Lukas Eisenmann, Zahra Monfared, Niclas Alexander Göring, Daniel Durstewitz

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Generalized Teacher Forcing for Learning Chaotic Dynamics.

Florian Hess, Zahra Monfared, Manuel Brenner, Daniel Durstewitz

Proceedings of the International Conference on Machine Learning, 2023

2022

Multimodal Teacher Forcing for Reconstructing Nonlinear Dynamical Systems.

Manuel Brenner, Georgia Koppe, Daniel Durstewitz

CoRR, 2022

On the difficulty of learning chaotic dynamics with RNNs.

Jonas M. Mikhaeil, Zahra Monfared, Daniel Durstewitz

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series.

Daniel Kramer, Philine Lou Bommer, Daniel Durstewitz, Carlo Tombolini, Georgia Koppe

Proceedings of the International Conference on Machine Learning, 2022

Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems.

Manuel Brenner, Florian Hess, Jonas M. Mikhaeil, Leonard F. Bereska, Zahra Monfared, Po-Chen Kuo, Daniel Durstewitz

Proceedings of the International Conference on Machine Learning, 2022

2021

Identifying nonlinear dynamical systems from multi-modal time series data.

Philine Lou Bommer, Daniel Kramer, Carlo Tombolini, Georgia Koppe, Daniel Durstewitz

CoRR, 2021

How to train RNNs on chaotic data?

Zahra Monfared, Jonas M. Mikhaeil, Daniel Durstewitz

CoRR, 2021

Identifying nonlinear dynamical systems with multiple time scales and long-range dependencies.

Dominik Schmidt, Georgia Koppe, Zahra Monfared, Max Beutelspacher, Daniel Durstewitz

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-time.

Zahra Monfared, Daniel Durstewitz

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

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

Georgia Koppe, Hazem Toutounji, Peter Kirsch, Stefanie Lis, Daniel Durstewitz

PLoS Comput. Biol., 2019

Inferring Dynamical Systems with Long-Range Dependencies through Line Attractor Regularization.

Dominik Schmidt, Georgia Koppe, Max Beutelspacher, Daniel Durstewitz

CoRR, 2019

LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos.

Elke Kirschbaum, Manuel Haußmann, Steffen Wolf, Hannah Sonntag, Justus Schneider, Shehabeldin Elzoheiry, Oliver Kann, Daniel Durstewitz, Fred A. Hamprecht

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Detecting Multiple Change Points Using Adaptive Regression Splines With Application to Neural Recordings.

Hazem Toutounji, Daniel Durstewitz

Frontiers Neuroinformatics, 2018

2017

A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements.

Daniel Durstewitz

PLoS Comput. Biol., 2017

Sparse convolutional coding for neuronal assembly detection.

Sven Peter, Elke Kirschbaum, Martin Both, Lee Campbell, Brandon Harvey, Conor Heins, Daniel Durstewitz, Ferran Diego, Fred A. Hamprecht

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

A Detailed Data-Driven Network Model of Prefrontal Cortex Reproduces Key Features of In Vivo Activity.

Joachim Hass, Loreen Hertäg, Daniel Durstewitz

PLoS Comput. Biol., 2016

Cell assemblies at multiple time scales with arbitrary lag distributions.

Eleonora Russo, Daniel Durstewitz

CoRR, 2016

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

Daniel Durstewitz

CoRR, 2016

2014

Analytical approximations of the firing rate of an adaptive exponential integrate-and-fire neuron in the presence of synaptic noise.

Loreen Hertäg, Daniel Durstewitz, Nicolas Brunel

Frontiers Comput. Neurosci., 2014

2012

An Approximation to the Adaptive Exponential Integrate-and-Fire Neuron Model Allows Fast and Predictive Fitting to Physiological Data.

Loreen Hertäg, Joachim Hass, Tatiana Golovko, Daniel Durstewitz

Frontiers Comput. Neurosci., 2012

2011

Models of dopaminergic modulation.

Joachim Hass, Daniel Durstewitz

Scholarpedia, 2011

Attracting Dynamics of Frontal Cortex Ensembles during Memory-Guided Decision-Making.

Emili Balaguer-Ballester, Christopher C. Lapish, Jeremy K. Seamans, Daniel Durstewitz

PLoS Comput. Biol., 2011

2009

Implications of synaptic biophysics for recurrent network dynamics and active memory.

Daniel Durstewitz

Neural Networks, 2009

2008

Dopamine modulation.

Jeremy K. Seamans, Daniel Durstewitz

Scholarpedia, 2008

2002

The computational role of dopamine D1 receptors in working memory.

Daniel Durstewitz, Jeremy K. Seamans

Neural Networks, 2002

1996

The Possible Function of Dopamine in Associative Learning: A Computational Model.

Daniel Durstewitz, Onur Güntürkün

Proceedings of the Artificial Neural Networks, 1996