jaxfne

Tensor-Field Neural Equations (TFNE) expressed as a JAX simulation engine for layer-resolved neural circuits, source operators, field proxies, probes, objectives, and evidence receipts.

Scientific grammar: Emitter → Source → Field → Probe → Objective → Optimizer → Manifest

Execution grammar: CircuitSpec → construct → Model → simulate → Signals

CircuitSpec is a conceptual category (Configuration | NeuronalTensor), not a concrete public production class — construct accepts either a Configuration (original path) or a NeuronalTensor with a RuntimeConfiguration (tensor-first path). It is unrelated to the experimental jaxfne.experimental_hpc.CircuitSpec type, which is not accepted by production construct.

Adaptation (optional HDP family): finite-dimensional hidden biophysical state $H$ and adaptive parameter coordinates $\Theta$ (synaptic and intrinsic), mediated by

$$ \dot X = F_X(X,H,\Theta,U),\quad \dot H = F_H(H,X,\Theta,U),\quad \dot\Theta = F_\Theta(H,X,\Theta). $$

$H$-state is the latent representation; HDP is the adaptive dynamical formulation that uses it. See H-state / HDP guide.

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)

Optional: generative development (JDNA)

Instead of loading a fixed tensor, generate one from a PseudoGenome:

genome  = jtfne.load_canonical_pseudogenome("canonical-v1-column-1000n")
tensor  = jtfne.develop(genome, seed=0)   # K_D: development seed
model   = jtfne.construct(tensor, jtfne.RuntimeConfiguration(seed=1, duration_ms=1000.0, dt_ms=0.5))
signals = jtfne.simulate(model)

JDNA is an optional path; the direct Configuration/NeuronalTensor paths remain first-class. See JDNA guide.

JaxFNE composes established neural dynamical models, source/readout operators, and JAX-native execution under the TFNE typed state/map grammar. See TFNE theory and References.

Import convention: import jaxfne as jtfne. Builder paths, paradigms, and optimization: Quickstart.

Relative and calibrated outputs

Simulated quantities are relative by default. Calibrated claims require an explicit transformation with evidence. See Scope & status.

Documentation

Resource	Link
Quickstart	docs/quickstart.md
Site	jaxfne.readthedocs.io
Tutorials	docs/tutorials/
Études	docs/etudes/
Frozen compatibility contract (0.4.13)	docs/public_surface_contract.md
Changelog	docs/changelog.md

Citation

CITATION.cff · citation guide

Visual examples

Real package outputs (simulated proxy scaffolds; calibration requires an explicit transformation with evidence): circuit geometry from Model.neuron_table(), a 10 s spike raster, a laminar spectrolaminar readout, and long-timescale firing-rate dynamics with homeostasis on/off. More: showcases.

_{Canonical laminar column — geometry from `neuron_table()`}	_{10 s spike raster — homeostasis on (proxy)}
_{Spectrolaminar proxy readout — depth-graded homeostasis}	_{Population rate over 10 s — homeostasis off vs on (r*=10 Hz)}

Release files for jaxfne 0.4.20

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for jaxfne 0.4.20
File	Size	Uploaded
jaxfne-0.4.20.tar.gz	36.7 MB	Sep 4, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for jaxfne 0.4.20
File	Interpreter	ABI	Platform	Reset
jaxfne-0.4.20-py3-none-any.whl 698.8 kB Sep 4, 2026	Python 3	none	any	Details

Total release size: 37.4 MB

Release files / jaxfne-0.4.20.tar.gz

Download URL	jaxfne-0.4.20.tar.gz
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Release files / jaxfne-0.4.20-py3-none-any.whl

Download URL	jaxfne-0.4.20-py3-none-any.whl
Size	698.8 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`3a048f605caf2251799fe347cd2811a689209041ad51a7e4f9dd3c181946470c`
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Upload date	Sep 4, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

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Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/HNXJ/jaxfne
Publishing commit: github.com/HNXJ/jaxfne/tree/e3b7d0adfe9fbac04e548ab9c8ae79693a87c296

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