Seminar, Hosted by Frontier Series 2026 — Örebro University and Lund University

Data-Driven Interpretable Dynamical Systems Models for Neuroscience & Psychiatry

Ernst Strüngmann Institute for Neuroscience, Frankfurt, Germany

Computational Neuroscience, Dynamical Systems and

Abstract

Brain function and psychiatric symptoms evolve dynamically. Manuel Brenner presents self-learning mathematical models that infer hidden states and patterns from brain activity and behaviour. Developed within LOEWE DYNAMIC, the interpretable models connect artificial intelligence, basic neuroscience and clinical psychiatry, seeking a path from temporal-data modelling to clinical application.

Topics

interpretable models

Source: codesign-lab.org

Data-Driven Interpretable Dynamical Systems Models for Neuroscience & Psychiatry

Abstract

Topics

Learning generative dynamical systems models from multi-modal and multi-animal neuro-data

Generative models of brain function: Inference, networks, and mechanisms

Predictive modeling, cortical hierarchy, and their computational implications

Meta-Dynamical State Space Models for Integrative Neural Data Analysis

Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics

Disentangling input dynamics from intrinsic neural dynamics in modeling of neural-behavioral data

Data-Driven Interpretable Dynamical Systems Models for Neuroscience & Psychiatry

Abstract

Topics

Related

Learning generative dynamical systems models from multi-modal and multi-animal neuro-data

Generative models of brain function: Inference, networks, and mechanisms

Predictive modeling, cortical hierarchy, and their computational implications

Meta-Dynamical State Space Models for Integrative Neural Data Analysis

Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics

Disentangling input dynamics from intrinsic neural dynamics in modeling of neural-behavioral data