Jaxley: differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics
Biophysical neuron models provide insights into cellular mechanisms underlying neural computations. A central challenge has been to identify parameters of detailed biophysical models such that they match physiological measurements or perform computational tasks. Here we describe a framework for simulating biophysical models in neuroscience-JAXLEY-which addresses this challenge. By making use of automatic differentiation and GPU acceleration, JAXLEY enables optimizing large-scale biophysical models with gradient descent. JAXLEY can learn biophysical neuron models to match voltage or two-photon calcium recordings, sometimes orders of magnitude more efficiently than previous methods. JAXLEY also makes it possible to train biophysical neuron models to perform computational tasks. We train a recurrent neural network to perform working memory tasks, and a network of morphologically detailed neurons with 100,000 parameters to solve a computer vision task. JAXLEY improves the ability to build large-scale data- or task-constrained biophysical models, creating opportunities for investigating the mechanisms underlying neural computations across multiple scales.
Authors
- Kyra L. Kadhim (ORCID: https://orcid.org/0000-0002-8524-2812)
- Cornelius Schröder (ORCID: https://orcid.org/0000-0001-5643-2097)
- Philipp Berens (ORCID: https://orcid.org/0000-0002-0199-4727)
- Ziwei Huang (ORCID: https://orcid.org/0000-0001-7517-0395)
- Matthijs Pals (ORCID: https://orcid.org/0000-0002-3051-1325)
- Pedro J. Gonçalves (ORCID: https://orcid.org/0000-0002-6987-4836)
- Janne K. Lappalainen (ORCID: https://orcid.org/0000-0002-0547-7401)
- Jakob H. Macke (ORCID: https://orcid.org/0000-0001-5154-8912)
- Michael Deistler (ORCID: https://orcid.org/0000-0002-3573-0404)
- Manuel Gloeckler
- James F. Beck (ORCID: https://orcid.org/0009-0000-0338-2559)
Institutions
- IMEC (BE)
- VIB-KU Leuven Center for Microbiology (BE)
- Bernstein Center for Computational Neuroscience Tübingen (DE)
- Max Planck Institute for Intelligent Systems (DE)
- Hertie Institute for Clinical Brain Research (DE)
- Neuroelectronics Research Flanders (BE)
- Tübingen AI Center
- University of Tübingen (DE)
- KU Leuven (BE)
Publication Details
- Journal
- Nature Methods
- Published
- 2025-11-13
- DOI
- https://doi.org/10.1038/s41592-025-02895-w
- Citations
- 5
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 12.40
Funders
- International Max Planck Research School for Environmental, Cellular and Molecular Microbiology
- European Commission
- Deutsche Forschungsgemeinschaft
- Eberhard Karls Universität Tübingen
- International Max Planck Research School for Advanced Methods in Process and Systems Engineering