jaxfne 0.3.24

JAX Field Neural Equations: a source-to-field neurophysiology engine for TFNE models.

jaxfne

JAX-based tools for TFNE source, field, probe, objective, and optimizer workflows.

Emitter -> Source -> Field -> Probe -> Objective -> Optimizer

jaxfne is built for compact computational biophysics tutorials and package-level experiments. Current tutorial readouts use simulated/proxy scales with JSON-safe reports and deterministic seeds.

Install

pip install -U jaxfne

Development checkout:

git clone https://github.com/HNXJ/jaxfne.git
cd jaxfne
pip install -e .[dev,viz,opt]

Minimal workflow

import jaxfne as jtfne

cfg = jtfne.Configuration()
cfg = cfg.runtime(seed=7, dtype="float32", duration_ms=1000.0, dt_ms=0.1)
cfg = cfg.column("single_neuron", layers=["L2/3"], n=1)
cfg = cfg.cell_types({"E": 1.0})
cfg = cfg.connectivity()
cfg = cfg.set_emitter("izhikevich", "cortical_eig")
cfg = cfg.probes(["MUA-proxy", "source-proxy", "LFP-proxy"])

model = jtfne.construct(cfg)
signals = jtfne.simulate(model, duration_ms=1000.0, dt_ms=0.1, seed=7)
print(signals.V_m.shape, signals.spikes.sum())

Multi-objective tuning

objectives = jtfne.rate_targets(
    groups={"first_half": range(24), "second_half": range(24, 48)},
    targets_hz={"first_half": 5.0, "second_half": 10.0},
)
optimizer = jtfne.agsdr(
    parameters={"drive_scale_a": (0.35, 2.25), "drive_scale_b": (0.35, 2.25)},
    generations=8,
    population_size=6,
    seed=42,
)
result = model.tune(objectives=objectives, optimizer=optimizer)
print(result.best_score, result.best_parameters)

Core readouts

Readout	Role
SPK	spike matrix or events
Vm	emitter voltage/state trace
Source	source/current proxy
LFP-proxy	laminar field proxy
CSD-proxy	spatial source/divergence proxy
EEG-proxy	linear scalp-channel proxy
MEG-proxy	linear magnetic-channel proxy
EMM-proxy	normalized activity-cost proxy

Validate a checkout

python -m compileall -q jaxfne tests examples
PYTEST_DISABLE_PLUGIN_AUTOLOAD=1 PYTHONPATH=. python -m pytest tests/ -q --tb=line
mkdocs build --strict

Documentation

• Quickstart
• Install
• Probe operators
• Tutorials
• API reference

MIT License.