jaxfne
JaxFNE is a Python package for biophysical source-field modeling, coupling neural activity and biophysical state with plasticity, geometry, and population- and field-scale dynamics.
JaxFNE is designed for models that need to change easily. Biology, dynamics, connectivity, geometry, and observations can be modified within the same model.
Workflow: change biology → change dynamics → simulate → measure
You can, for example:
- add or change biophysical state $H$;
- change dynamics, plasticity, connectivity, or geometry;
- change model detail;
- measure spikes, population activity, or fields;
- develop or reduce models with JDNA.
Install
pip install jaxfne
pip install "jaxfne[viz]" # optional plotting
Development: pip install -e ".[dev,viz]" after cloning.
Minimal example
import jaxfne as jtfne
jtfne.enable_x64()
tensor = jtfne.load_canonical_neuronal_tensor("canonical-v1-column-1000n")
model = jtfne.construct(tensor, jtfne.RuntimeConfiguration(seed=0, duration_ms=1000.0, dt_ms=0.5))
signals = jtfne.simulate(model)
Import convention: import jaxfne as jtfne.
Documentation
| Resource | Link |
|---|---|
| Quickstart | docs/quickstart.md |
| Site | jaxfne.readthedocs.io |
| Tutorials | docs/tutorials/ |
| Études | docs/etudes/ |
| Public API surface (0.4.13) | docs/public_surface_contract.md |
| Changelog | docs/changelog.md |
If you are an AI agent, read artifacts/AGENTS.md.
Citation
Canonical Visualization Atlas
Six linked panels (network_3d, connectivity, raster, traces, spectral, state_summary) separate OBSERVED from DERIVED quantities and attach
manifest provenance. Previews and generation code:
documentation site and
Atlas guide.
Release files for jaxfne 0.4.22
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| jaxfne-0.4.22.tar.gz | 38.1 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| jaxfne-0.4.22-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 38.8 MB
Release files / jaxfne-0.4.22.tar.gz
| Download URL | jaxfne-0.4.22.tar.gz |
|---|---|
| Size | 38.1 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Transparency logRelease files / jaxfne-0.4.22-py3-none-any.whl
| Download URL | jaxfne-0.4.22-py3-none-any.whl |
|---|---|
| Size | 709.2 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 11, 2026.
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