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 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.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. 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.