Daniel Durstewitz

Publication Activity (10 Years)

Years Active: 1996-2024
Publications (10 Years): 27

Top Topics

Recurrent Neural Networks

Dynamical Systems

Prefrontal Cortex

Top Venues

PLoS Comput. Biol.

Publications

Christoph Jürgen Hemmer, Manuel Brenner, Florian Hess, Daniel Durstewitz
Optimal Recurrent Network Topologies for Dynamical Systems Reconstruction. CoRR (2024)
Han Cao, Sivanesan Rajan, Bianka Hahn, Ersoy Kocak, Daniel Durstewitz, Emanuel Schwarz, Verena Schneider-Lindner
MTLComb: multi-task learning combining regression and classification tasks for joint feature selection. CoRR (2024)
Niclas Alexander Göring, Florian Hess, Manuel Brenner, Zahra Monfared, Daniel Durstewitz
Out-of-Domain Generalization in Dynamical Systems Reconstruction. CoRR (2024)
Egzon Miftari, Daniel Durstewitz, Filip Sadlo
Visualization of Discontinuous Vector Field Topology. IEEE Trans. Vis. Comput. Graph. 30 (1) (2024)
Florian Hess, Zahra Monfared, Manuel Brenner, Daniel Durstewitz
Generalized Teacher Forcing for Learning Chaotic Dynamics. CoRR (2023)
Lukas Eisenmann, Zahra Monfared, Niclas Alexander Göring, Daniel Durstewitz
Bifurcations and loss jumps in RNN training. CoRR (2023)
Lukas Eisenmann, Zahra Monfared, Niclas Alexander Göring, Daniel Durstewitz
Bifurcations and loss jumps in RNN training. NeurIPS (2023)
Florian Hess, Zahra Monfared, Manuel Brenner, Daniel Durstewitz
Generalized Teacher Forcing for Learning Chaotic Dynamics. ICML (2023)
Manuel Brenner, Florian Hess, Jonas M. Mikhaeil, Leonard F. Bereska, Zahra Monfared, Po-Chen Kuo, Daniel Durstewitz
Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems. ICML (2022)
Manuel Brenner, Florian Hess, Jonas M. Mikhaeil, Leonard F. Bereska, Zahra Monfared, Po-Chen Kuo, Daniel Durstewitz
Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems. CoRR (2022)
Jonas M. Mikhaeil, Zahra Monfared, Daniel Durstewitz
On the difficulty of learning chaotic dynamics with RNNs. NeurIPS (2022)
Daniel Kramer, Philine Lou Bommer, Daniel Durstewitz, Carlo Tombolini, Georgia Koppe
Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series. ICML (2022)
Manuel Brenner, Georgia Koppe, Daniel Durstewitz
Multimodal Teacher Forcing for Reconstructing Nonlinear Dynamical Systems. CoRR (2022)
Philine Lou Bommer, Daniel Kramer, Carlo Tombolini, Georgia Koppe, Daniel Durstewitz
Identifying nonlinear dynamical systems from multi-modal time series data. CoRR (2021)
Zahra Monfared, Jonas M. Mikhaeil, Daniel Durstewitz
How to train RNNs on chaotic data? CoRR (2021)
Dominik Schmidt, Georgia Koppe, Zahra Monfared, Max Beutelspacher, Daniel Durstewitz
Identifying nonlinear dynamical systems with multiple time scales and long-range dependencies. ICLR (2021)
Zahra Monfared, Daniel Durstewitz
Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-time. ICML (2020)
Dominik Schmidt, Georgia Koppe, Max Beutelspacher, Daniel Durstewitz
Inferring Dynamical Systems with Long-Range Dependencies through Line Attractor Regularization. CoRR (2019)
Georgia Koppe, Hazem Toutounji, Peter Kirsch, Stefanie Lis, Daniel Durstewitz
Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI. PLoS Comput. Biol. 15 (8) (2019)
Elke Kirschbaum, Manuel Haußmann, Steffen Wolf, Hannah Sonntag, Justus Schneider, Shehabeldin Elzoheiry, Oliver Kann, Daniel Durstewitz, Fred A. Hamprecht
LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos. ICLR (Poster) (2019)
Georgia Koppe, Hazem Toutounji, Peter Kirsch, Stefanie Lis, Daniel Durstewitz
Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI. CoRR (2019)
Hazem Toutounji, Daniel Durstewitz
Detecting Multiple Change Points Using Adaptive Regression Splines With Application to Neural Recordings. Frontiers Neuroinformatics 12 (2018)
Sven Peter, Elke Kirschbaum, Martin Both, Lee Campbell, Brandon Harvey, Conor Heins, Daniel Durstewitz, Ferran Diego, Fred A. Hamprecht
Sparse convolutional coding for neuronal assembly detection. NIPS (2017)
Daniel Durstewitz
A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements. PLoS Comput. Biol. 13 (6) (2017)
Eleonora Russo, Daniel Durstewitz
Cell assemblies at multiple time scales with arbitrary lag distributions. CoRR (2016)
Daniel Durstewitz
A State Space Approach for Piecewise-Linear Recurrent Neural Networks for Reconstructing Nonlinear Dynamics from Neural Measurements. CoRR (2016)
Joachim Hass, Loreen Hertäg, Daniel Durstewitz
A Detailed Data-Driven Network Model of Prefrontal Cortex Reproduces Key Features of In Vivo Activity. PLoS Comput. Biol. 12 (5) (2016)
Loreen Hertäg, Daniel Durstewitz, Nicolas Brunel
Analytical approximations of the firing rate of an adaptive exponential integrate-and-fire neuron in the presence of synaptic noise. Frontiers Comput. Neurosci. 8 (2014)
Loreen Hertäg, Joachim Hass, Tatiana Golovko, Daniel Durstewitz
An Approximation to the Adaptive Exponential Integrate-and-Fire Neuron Model Allows Fast and Predictive Fitting to Physiological Data. Frontiers Comput. Neurosci. 6 (2012)
Joachim Hass, Daniel Durstewitz
Models of dopaminergic modulation. Scholarpedia 6 (8) (2011)
Emili Balaguer-Ballester, Christopher C. Lapish, Jeremy K. Seamans, Daniel Durstewitz
Attracting Dynamics of Frontal Cortex Ensembles during Memory-Guided Decision-Making. PLoS Comput. Biol. 7 (5) (2011)
Daniel Durstewitz
Implications of synaptic biophysics for recurrent network dynamics and active memory. Neural Networks 22 (8) (2009)
Jeremy K. Seamans, Daniel Durstewitz
Dopamine modulation. Scholarpedia 3 (4) (2008)
Daniel Durstewitz, Jeremy K. Seamans
The computational role of dopamine D1 receptors in working memory. Neural Networks 15 (4-6) (2002)
Daniel Durstewitz, Onur Güntürkün
The Possible Function of Dopamine in Associative Learning: A Computational Model. ICANN (1996)