80 citations · 107 across the 8 of their papers we have counts for
10 papers
Continual Learning of Dynamical Systems in Recurrent Neural Networks through Recyclable Unit Gating
Sima Hashemi, Daniel Durstewitz, Georgia Koppe
Dynamical Systems Reconstruction (DSR) aims to infer models from observed time series that reproduce a system's qualitative long-term behavior. Continual DSR (cDSR) requires learni…
What Neuroscience Can Teach AI About Learning in Continuously Changing Environments
Daniel Durstewitz, Bruno Averbeck, Georgia Koppe
Modern AI models, such as large language models, are usually trained once on a huge corpus of data, potentially fine-tuned for a specific task, and then deployed with fixed paramet…
Uncovering the Computational Roles of Nonlinearity in Sequence Modeling Using Almost-Linear RNNs
Manuel Brenner, Georgia Koppe
Sequence modeling tasks across domains such as natural language processing, time series forecasting, and control require learning complex input-output mappings. Nonlinear recurrenc…
A scalable generative model for dynamical system reconstruction from neuroimaging data
Eric Volkmann, Alena Brändle, Daniel Durstewitz +1
Data-driven inference of the generative dynamics underlying a set of observed time series is of growing interest in machine learning and the natural sciences. In neuroscience, such…
Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data
Manuel Brenner, Elias Weber, Georgia Koppe +1
In science, we are often interested in obtaining a generative model of the underlying system dynamics from observed time series. While powerful methods for dynamical systems recons…
Integrating Multimodal Data for Joint Generative Modeling of Complex Dynamics
Manuel Brenner, Florian Hess, Georgia Koppe +1
Many, if not most, systems of interest in science are naturally described as nonlinear dynamical systems. Empirically, we commonly access these systems through time series measurem…
Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series
Daniel Kramer, Philine Lou Bommer, Carlo Tombolini +2
Empirically observed time series in physics, biology, or medicine, are commonly generated by some underlying dynamical system (DS) which is the target of scientific interest. There…
Identifying nonlinear dynamical systems with multiple time scales and long-range dependencies
Dominik Schmidt, Georgia Koppe, Zahra Monfared +2
A main theoretical interest in biology and physics is to identify the nonlinear dynamical system (DS) that generated observed time series. Recurrent Neural Networks (RNNs) are, in…
Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI
Georgia Koppe, Hazem Toutounji, Peter Kirsch +2
A major tenet in theoretical neuroscience is that cognitive and behavioral processes are ultimately implemented in terms of the neural system dynamics. Accordingly, a major aim for…
Psychiatric Illnesses as Disorders of Network Dynamics
Daniel Durstewitz, Quentin J. M. Huys, Georgia Koppe
This review provides a dynamical systems perspective on psychiatric symptoms and disease, and discusses its potential implications for diagnosis, prognosis, and treatment. After a…