snnlab

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

uv add snnlab

from snnlab import analysis, 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.
4. snnlab.analysis measures population activity, rhythmicity and conductance trajectories from recorded NumPy data.
5. snnlab.extensions registers versioned Python definitions for custom dynamics, weights, operations, training and encoders.

The graph simulator supports COBA-LIF, current-based LIF and leaky-integrator populations, conductance and exponential-current synapses, recurrent/feedback connections and integer-timestep delays. Registered Python callbacks add custom models without embedding code in bundles; import their registration module before compilation or execution. See examples/current-lif/current_lif.py for a built-in current-based simulation and examples/customisation/customisation.py for an adaptive current neuron and custom weight distribution.

Simulator commands

uv run snnsim --help
uv run python -m snnlab.sim sim --help

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 Installation and Quickstart guides.

cd docs
bun install --frozen-lockfile
bun run dev

Compatibility

Release history is recorded in CHANGELOG.md. The package version is defined in src/snnlab/__init__.py; Hatchling reads it when building.

To prepare a release, update that version and move the Unreleased changelog notes into a dated section matching the new version. Use patch versions for compatible fixes and minor versions for new functionality or breaking changes before 1.0. Changes to public APIs, numerical defaults or persisted formats need explicit changelog notes.

After checks pass, pushing the version change to main triggers the publishing workflow, which publishes to PyPI and creates the matching v<VERSION> Git tag. Publishing requires the repository's pypi environment and a PyPI Trusted Publisher configured for .github/workflows/publish.yml. The workflow can also be run manually to retry a release.

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.