Dynamical
Inference Lab
Understanding biological computation through machine learning.
We are a research group led by Steffen Schneider at the Institute of Computational Biology, Helmholtz Munich. We develop robust, identifiable and interpretable machine learning methods that connect representation learning with the inference of nonlinear dynamics. We work on the foundations of these methods and apply them to questions from systems neuroscience, cell biology and other areas of the life sciences.
Team
The team includes researchers and engineers from machine learning and the life sciences.
Team members and collaboratorsFunding
This work is supported by the following funders and grants.
Helmholtz Imaging
TIMELY
A shared benchmark for biological time series, bringing datasets and evaluation methods together to study complex dynamics across modalities.
Helmholtz AI
BrainAIcast
Multimodal AI for online neuronal perturbation: learning from neural recordings to predict brain states and guide targeted interventions in preclinical models.