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
[ \text{Emitter} \rightarrow \text{Source} \rightarrow \text{Field} \rightarrow \text{Probe} \rightarrow \text{Objective} \rightarrow \text{Optimizer} \rightarrow \text{Evidence} ]
Execution grammar
[ \text{CircuitSpec} \rightarrow \texttt{construct} \rightarrow \texttt{Model} \rightarrow \texttt{simulate} \rightarrow \texttt{Signals} ]
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)
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/ |
| Public API contract (0.4.13) | docs/public_surface_contract.md |
| Changelog | docs/changelog.md |
Citation
1 maintainer
Release files for jaxfne 0.4.15
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-0.4.15.tar.gz | 30.2 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| jaxfne-0.4.15-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 30.7 MB
Release files / jaxfne-0.4.15.tar.gz
| Download URL | jaxfne-0.4.15.tar.gz |
|---|---|
| Size | 30.2 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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twine/6.2.0 CPython/3.13.7
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Release files / jaxfne-0.4.15-py3-none-any.whl
| Download URL | jaxfne-0.4.15-py3-none-any.whl |
|---|---|
| Size | 490.7 kB |
| Tags | Python 3 |
|
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No |
| Uploaded via |
twine/6.2.0 CPython/3.13.7
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