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.

Quickstart · Scope & status

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

CITATION.cff · citation guide

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.