Jonas Beck

Jonas Beck
Jonas Beck is interested in probabilistic inference in mechanistic models of neuroscience. More specifically, he applies probabilistic numerical methods and simulation-based inference to fit Hodgkin-Huxley models to observed (experimental) data as part of the PIMMS Network Project of the Cluster of Excellence - Machine Learning for Science.
Mechanistic models allow to make precise and testable predictions about the underlying biophysical mechanisms that influence neural activity.
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Beck, J., Bosch, N., Deistler, M., Kadhim, K.L., Macke, J. H., Hennig, P., Berens, P.

Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations

Jun 25, 2024 | International Conference on Machine Learning 2024
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Beck, J., Deistler, M., Bernaerts, Y., Macke, J. H., & Berens, P

Efficient identification of informative features in simulation-based inference

Feb 24, 2022 | Advances in Neural Information Processing Systems, 35, 19260-19273