jaxfne
JaxFNE is a Python package for biophysical source-field modeling. It couples neural activity and biophysical state with plasticity, geometry, and population- and field-scale dynamics, and each of these can be changed within one model.
Workflow: change biology → change dynamics → simulate → measure
- State: add or change biophysical state $H$.
- Mechanism: change dynamics, plasticity, connectivity, geometry, or model detail.
- Observation: measure spikes, population activity, or fields.
- Reduction: 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 | docs/public_surface_contract.md |
| Changelog | docs/changelog.md |
If you are an AI agent, read artifacts/AGENTS.md.
Citation
Canonical Visualization Atlas
Seven linked panels (schema, network_3d, raster, lfp, h_dynamics, hdp, oscillatory) label each quantity OBSERVED or DERIVED and carry
manifest provenance. A panel whose declared inputs are missing renders an omission card. Previews and generation code:
documentation site and
Atlas guide.
1 maintainer
Metadata
1 maintainer
Release files for jaxfne 0.5.5
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-0.5.5.tar.gz | 46.4 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| jaxfne-0.5.5-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 47.2 MB
Release files / jaxfne-0.5.5.tar.gz
| Download URL | jaxfne-0.5.5.tar.gz |
|---|---|
| Size | 46.4 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
546ccf0348ae97d1a94a985e96b810d52f9c0da3347b55d2da8a57bea0f91f69
|
|
BLAKE2b-256 checksum How to use checksums |
7fb8ccc3d9841ba8b4425664795f1dff3472dc3fe8f4c3cd41833b396841edaa
|
| Upload date | |
|
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 Oct 11, 2026.
Transparency logRelease files / jaxfne-0.5.5-py3-none-any.whl
| Download URL | jaxfne-0.5.5-py3-none-any.whl |
|---|---|
| Size | 746.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
496a8a310938334dda490e364bd5c3e945e7bb58ce6cb9d2d893b803ebfdf2ce
|
|
BLAKE2b-256 checksum How to use checksums |
3962ce6f183bb4f811203a24a8592e78c1176599ffb3cf4779846ed6eef3291b
|
| Upload date | |
|
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 Oct 11, 2026.
Transparency log
