jaxfne
JAX-based simulation of Tensor-Field Neural Equations — emitter-to-source-to-field readouts for computational electrophysiology. Define a circuit (single column or multi-area hierarchy), run it, and inspect population activity, layer-targeted drive, and field readouts in one pipeline.
jaxfne works at the population/field scale — layer-resolved circuits, spectrolaminar readouts, a tensor-algebraic source-to-sensor-proxy chain. For single/multi-compartment biophysical detail, Jaxley is the natural complement, not a competing tool: Jaxley models plug directly into jaxfne as emitters (see the Jaxley bridge in Quickstart).
Install
pip install jaxfne
pip install "jaxfne[viz]" # matplotlib/plotly readouts
Development checkout: 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)
jtfne.vis.raster(signals) # population raster
jtfne.vis.spectrolaminar_suite(signals) # laminar PSD readout
Canonical import: import jaxfne as jtfne. More paths (fluent Configuration,
multi-trial sweeps, HDP plasticity, Jaxley bridge): Quickstart.
Scope & status
Every jaxfne output is labeled Relative or Absolute. Relative values are the default and require no external evidence; Absolute (physically calibrated) values require an explicit, evidenced calibration step. Reference: Scope & status.
Exported but not yet implemented: GLIFEmitter, LIFEmitter, write_nwb, read_nwb.
Documentation
| Resource | Link |
|---|---|
| Quickstart (three build paths, canonical column, Jaxley) | docs/quickstart.md |
| Full docs site | jaxfne.readthedocs.io |
| Tutorials & études | docs/tutorials/ |
| Changelog | docs/changelog.md |
| Contributing | docs/contributing.md |
| AI agents | docs/for_ai_agents.md |
Documentation for AI coding agents (skills/, docs/for_ai_agents.md) is a deliberate design choice, not incidental repo clutter — it's verified against the same source of truth as the human-facing docs above, not a separate, drifting spec.
Citation
Machine-readable metadata: CITATION.cff (GitHub Cite this repository).
BibTeX and Zenodo DOI setup: docs/citation.md.
1 maintainer
Release files for jaxfne 0.4.7
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-0.4.7.tar.gz | 16.3 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| jaxfne-0.4.7-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 16.7 MB
Release files / jaxfne-0.4.7.tar.gz
| Download URL | jaxfne-0.4.7.tar.gz |
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| Size | 16.3 MB |
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| Tags | Python 3 |
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