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jaxfne

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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

CITATION.cff · citation guide

Release files for jaxfne 0.4.14

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