snnlab

A Python library for authoring, simulating and visualising conductance-based spiking neural networks.

uv add git+https://github.com/eoinmurray/snnlab

from snnlab import lang, sim, viz

1. snnlab.lang authors and validates deterministic graph bundles.
2. snnlab.sim executes graphs and supports surrogate-gradient training.
3. snnlab.viz renders recordings, diagrams, figures and animations.

The simulator currently supports COBA-LIF and leaky-integrator graph populations, AMPA/GABA projections, recurrent/feedback connections and integer-timestep delays. It is not an arbitrary-equation simulator.

Simulator commands

uv run snnsim --help
uv run python -m snnlab.sim sim --help
uv run python -m snnlab.lang.examples.build_examples

Graphviz (dot) is required for diagram exports. FFmpeg is required for video exports. Install these separately through your operating system package manager. Core authoring and simulation do not require either executable.

Development

uv sync --dev
uv run pytest -m "not slow"

For development in Pinglab, use uv add --editable ../snnlab. For reproducible runs, use a Git tag or commit and commit the consumer lockfile.

Documentation

The Fumadocs site lives in docs/, with guides for authoring, simulation, training, visualisation, and scientific contracts.

cd docs
npm ci
npm run dev

Compatibility

Release history is recorded in CHANGELOG.md. See VERSIONING.md for the version policy, release preparation and tagging workflow. Check release metadata with uv run python scripts/version.py check.

This initial extraction retains the existing bundle schemas, backend target tools/snnsim, component format versions and numerical defaults. Those strings identify persisted scientific contracts; they are not Python import paths. Package version 0.1.0 identifies the combined distribution.

The retained-data regression against historical Pinglab runs remains in Pinglab. The portable tests live here. No scientific runs or generated example bundles are shipped.

MIT licensed.