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ORCIDDaniel Durstewitz
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2020 – today
- 2024
[j12]Egzon Miftari
, Daniel Durstewitz, Filip Sadlo:
Visualization of Discontinuous Vector Field Topology. IEEE Trans. Vis. Comput. Graph. 30(1): 45-54 (2024)- [c13]Manuel Brenner, Florian Hess, Georgia Koppe, Daniel Durstewitz:
Integrating Multimodal Data for Joint Generative Modeling of Complex Dynamics. ICML 2024 - [c12]Niclas Alexander Göring, Florian Hess, Manuel Brenner, Zahra Monfared, Daniel Durstewitz:
Out-of-Domain Generalization in Dynamical Systems Reconstruction. ICML 2024 - [c11]Christoph Jürgen Hemmer, Manuel Brenner, Florian Hess, Daniel Durstewitz:
Optimal Recurrent Network Topologies for Dynamical Systems Reconstruction. ICML 2024 - [i15]Niclas Alexander Göring, Florian Hess, Manuel Brenner, Zahra Monfared, Daniel Durstewitz:
Out-of-Domain Generalization in Dynamical Systems Reconstruction. CoRR abs/2402.18377 (2024) - [i14]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 abs/2405.09886 (2024) - [i13]Christoph Jürgen Hemmer, Manuel Brenner, Florian Hess, Daniel Durstewitz:
Optimal Recurrent Network Topologies for Dynamical Systems Reconstruction. CoRR abs/2406.04934 (2024) - [i12]Manuel Brenner, Elias Weber, Georgia Koppe, Daniel Durstewitz:
Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data. CoRR abs/2410.04814 (2024) - [i11]Manuel Brenner, Christoph Jürgen Hemmer, Zahra Monfared, Daniel Durstewitz:
Almost-Linear RNNs Yield Highly Interpretable Symbolic Codes in Dynamical Systems Reconstruction. CoRR abs/2410.14240 (2024) - 2023
- [c10]Florian Hess, Zahra Monfared, Manuel Brenner, Daniel Durstewitz:
Generalized Teacher Forcing for Learning Chaotic Dynamics. ICML 2023: 13017-13049 - [c9]Lukas Eisenmann, Zahra Monfared, Niclas Alexander Göring, Daniel Durstewitz:
Bifurcations and loss jumps in RNN training. NeurIPS 2023 - [i10]Florian Hess, Zahra Monfared, Manuel Brenner, Daniel Durstewitz:
Generalized Teacher Forcing for Learning Chaotic Dynamics. CoRR abs/2306.04406 (2023) - [i9]Lukas Eisenmann, Zahra Monfared, Niclas Alexander Göring, Daniel Durstewitz:
Bifurcations and loss jumps in RNN training. CoRR abs/2310.17561 (2023) - 2022
- [c8]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: 2292-2320 - [c7]Daniel Kramer, Philine Lou Bommer, Daniel Durstewitz, Carlo Tombolini, Georgia Koppe:
Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series. ICML 2022: 11613-11633 - [c6]Jonas M. Mikhaeil, Zahra Monfared, Daniel Durstewitz:
On the difficulty of learning chaotic dynamics with RNNs. NeurIPS 2022 - [i8]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 abs/2207.02542 (2022) - [i7]Manuel Brenner, Georgia Koppe, Daniel Durstewitz:
Multimodal Teacher Forcing for Reconstructing Nonlinear Dynamical Systems. CoRR abs/2212.07892 (2022) - 2021
- [c5]Dominik Schmidt, Georgia Koppe, Zahra Monfared, Max Beutelspacher, Daniel Durstewitz:
Identifying nonlinear dynamical systems with multiple time scales and long-range dependencies. ICLR 2021 - [i6]Zahra Monfared, Jonas M. Mikhaeil, Daniel Durstewitz:
How to train RNNs on chaotic data? CoRR abs/2110.07238 (2021) - [i5]Philine Lou Bommer, Daniel Kramer, Carlo Tombolini, Georgia Koppe, Daniel Durstewitz:
Identifying nonlinear dynamical systems from multi-modal time series data. CoRR abs/2111.02922 (2021) - 2020
- [c4]Zahra Monfared, Daniel Durstewitz:
Transformation of ReLU-based recurrent neural networks from discrete-time to continuous-time. ICML 2020: 6999-7009
2010 – 2019
- 2019
- [j11]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) - [c3]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 - [i4]Georgia Koppe, Hazem Toutounji, Peter Kirsch, Stefanie Lis, Daniel Durstewitz:
Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI. CoRR abs/1902.07186 (2019) - [i3]Dominik Schmidt, Georgia Koppe, Max Beutelspacher, Daniel Durstewitz:
Inferring Dynamical Systems with Long-Range Dependencies through Line Attractor Regularization. CoRR abs/1910.03471 (2019) - 2018
- [j10]Hazem Toutounji, Daniel Durstewitz:
Detecting Multiple Change Points Using Adaptive Regression Splines With Application to Neural Recordings. Frontiers Neuroinformatics 12: 67 (2018) - 2017
- [j9]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) - [c2]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: 3675-3685 - 2016
- [j8]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) - [i2]Eleonora Russo, Daniel Durstewitz:
Cell assemblies at multiple time scales with arbitrary lag distributions. CoRR abs/1607.00969 (2016) - [i1]Daniel Durstewitz:
A State Space Approach for Piecewise-Linear Recurrent Neural Networks for Reconstructing Nonlinear Dynamics from Neural Measurements. CoRR abs/1612.07846 (2016) - 2014
- [j7]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: 116 (2014) - 2012
- [j6]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: 62 (2012) - 2011
- [j5]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) - [j4]Joachim Hass, Daniel Durstewitz:
Models of dopaminergic modulation. Scholarpedia 6(8): 4215 (2011)
2000 – 2009
- 2009
- [j3]Daniel Durstewitz:
Implications of synaptic biophysics for recurrent network dynamics and active memory. Neural Networks 22(8): 1189-1200 (2009) - 2008
- [j2]Jeremy K. Seamans, Daniel Durstewitz:
Dopamine modulation. Scholarpedia 3(4): 2711 (2008) - 2002
- [j1]Daniel Durstewitz, Jeremy K. Seamans:
The computational role of dopamine D1 receptors in working memory. Neural Networks 15(4-6): 561-572 (2002)
1990 – 1999
- 1996
- [c1]Daniel Durstewitz, Onur Güntürkün:
The Possible Function of Dopamine in Associative Learning: A Computational Model. ICANN 1996: 667-672
Coauthor Index
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last updated on 2024-11-28 21:24 CET by the dblp team
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