The Possible Function of Dopamine in Associative Learning: A Computational Model. - Daniel Durstewitz; Onur Güntürkün (ICANN, 1996)

The computational role of dopamine D1 receptors in working memory. - Daniel Durstewitz; Jeremy K. Seamans (Neural Networks, 2002)

Dopamine modulation. - Jeremy K. Seamans; Daniel Durstewitz (Scholarpedia, 2008)

Implications of synaptic biophysics for recurrent network dynamics and active memory. - Daniel Durstewitz (Neural Networks, 2009)

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)

Models of dopaminergic modulation. - Joachim Hass; Daniel Durstewitz (Scholarpedia, 2011)

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)

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)

A State Space Approach for Piecewise-Linear Recurrent Neural Networks for Reconstructing Nonlinear Dynamics from Neural Measurements. - Daniel Durstewitz (CoRR, 2016)

Cell assemblies at multiple time scales with arbitrary lag distributions. - Eleonora Russo; Daniel Durstewitz (CoRR, 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)

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 (NIPS, 2017)

A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements. - Daniel Durstewitz (PLoS Comput. Biol., 2017)

Detecting Multiple Change Points Using Adaptive Regression Splines With Application to Neural Recordings. - Hazem Toutounji; Daniel Durstewitz (Frontiers Neuroinformatics, 2018)

LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos. - Elke Kirschbaum; Manuel Haußmann; Steffen Wolf 001; Hannah Sonntag; Justus Schneider; Shehabeldin Elzoheiry; Oliver Kann; Daniel Durstewitz; Fred A. Hamprecht (ICLR (Poster), 2019)

Inferring Dynamical Systems with Long-Range Dependencies through Line Attractor Regularization. - Dominik Schmidt; Georgia Koppe; Max Beutelspacher; Daniel Durstewitz (CoRR, 2019)

Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI. - Georgia Koppe; Hazem Toutounji; Peter Kirsch 001; Stefanie Lis; Daniel Durstewitz (PLoS Comput. Biol., 2019)

Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-time. - Zahra Monfared; Daniel Durstewitz (ICML, 2020)

Identifying nonlinear dynamical systems with multiple time scales and long-range dependencies. - Dominik Schmidt; Georgia Koppe; Zahra Monfared; Max Beutelspacher; Daniel Durstewitz (ICLR, 2021)

How to train RNNs on chaotic data? - Zahra Monfared; Jonas M. Mikhaeil; Daniel Durstewitz (CoRR, 2021)

Identifying nonlinear dynamical systems from multi-modal time series data. - Philine Lou Bommer; Daniel Kramer; Carlo Tombolini; Georgia Koppe; Daniel Durstewitz (CoRR, 2021)

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 (ICML, 2022)

Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series. - Daniel Kramer; Philine Lou Bommer; Daniel Durstewitz; Carlo Tombolini; Georgia Koppe (ICML, 2022)

On the difficulty of learning chaotic dynamics with RNNs. - Jonas M. Mikhaeil; Zahra Monfared; Daniel Durstewitz (NeurIPS, 2022)

Multimodal Teacher Forcing for Reconstructing Nonlinear Dynamical Systems. - Manuel Brenner; Georgia Koppe; Daniel Durstewitz (CoRR, 2022)

Generalized Teacher Forcing for Learning Chaotic Dynamics. - Florian Hess; Zahra Monfared; Manuel Brenner; Daniel Durstewitz (ICML, 2023)

Bifurcations and loss jumps in RNN training. - Lukas Eisenmann; Zahra Monfared; Niclas Alexander Göring; Daniel Durstewitz (NeurIPS, 2023)

Integrating Multimodal Data for Joint Generative Modeling of Complex Dynamics. - Manuel Brenner; Florian Hess; Georgia Koppe; Daniel Durstewitz (ICML, 2024)

Out-of-Domain Generalization in Dynamical Systems Reconstruction. - Niclas Alexander Göring; Florian Hess; Manuel Brenner; Zahra Monfared; Daniel Durstewitz (ICML, 2024)

Optimal Recurrent Network Topologies for Dynamical Systems Reconstruction. - Christoph Jürgen Hemmer; Manuel Brenner; Florian Hess; Daniel Durstewitz (ICML, 2024)

Almost-Linear RNNs Yield Highly Interpretable Symbolic Codes in Dynamical Systems Reconstruction. - Manuel Brenner; Christoph Jürgen Hemmer; Zahra Monfared; Daniel Durstewitz (NeurIPS, 2024)

A scalable generative model for dynamical system reconstruction from neuroimaging data. - Eric Volkmann; Alena Brändle; Daniel Durstewitz; Georgia Koppe (NeurIPS, 2024)

MTLComb: multi-task learning combining regression and classification tasks for joint feature selection. - Han Cao 001; Sivanesan Rajan; Bianka Hahn; Ersoy Kocak; Daniel Durstewitz; Emanuel Schwarz; Verena Schneider-Lindner (CoRR, 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)

Visualization of Discontinuous Vector Field Topology. - Egzon Miftari; Daniel Durstewitz; Filip Sadlo (IEEE Trans. Vis. Comput. Graph., 2024)

Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data. - Manuel Brenner; Elias Weber; Georgia Koppe; Daniel Durstewitz (ICLR, 2025)

True Zero-Shot Inference of Dynamical Systems Preserving Long-Term Statistics. - Christoph Jürgen Hemmer; Daniel Durstewitz (CoRR, 2025)

What Neuroscience Can Teach AI About Learning in Continuously Changing Environments. - Daniel Durstewitz; Bruno Averbeck; Georgia Koppe (CoRR, 2025)