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name: snntorch description: "Use snnTorch to build spiking neuron models, encode data into spikes, train with surrogate gradients, plot spike tensors, export/import NIR graphs, and maintain legacy spikevision workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

snnTorch

Use this skill when a task touches the snntorch package: stateful spiking neurons, spike encoders, surrogate gradients, training helpers, plotting, NIR exchange, or legacy neuromorphic datasets.

Install and import check

Read Install and import for the package-level install matrix. For a quick smoke test, run:

pip install snntorch
python -c "import snntorch as snn; print(snn.__version__)"
python scripts/stack_smoke.py

If you need the broader stack for plotting, NIR, or legacy dataset workflows, add the optional dependencies described in that reference.

Route by task

• Build, combine, or debug neuron and layer workflows, hidden-state ownership, reset semantics, recurrent cells, BNTT, or GradedSpikes -> core-neurons
• Convert raw tensors or labels into spikes, choose surrogate gradients, compute losses or accuracies, inspect monitors, run backprop wrappers, or use STDP -> encoding-training
• Export supported models to NIR or import NIR graphs back into snnTorch -> nir-interoperability
• Plot rasters, spike counts, traces, or animations -> plotting
• Use the legacy spikevision dataset wrappers or migrate old neuromorphic dataset code -> spikevision

Common starting points

• If the model returns the wrong number of values, states leak across batches, or nn.Sequential breaks on tuple outputs, start with core-neurons.
• If the task starts with tensors or labels and ends with spike codes, a loss, or a training step, start with encoding-training.
• If export_to_nir or import_from_nir fails with type inference or graph-shape errors, start with nir-interoperability.
• If plotting fails in a headless environment or spike_count labels behave oddly, start with plotting.
• If an old tutorial or repo script uses snntorch.spikevision, treat it as legacy and start with spikevision; new work should use Tonic instead.

Shared references

• Install and import
• Workflow map
• Cross-cutting troubleshooting
• Repository provenance
• Routing metadata
• scripts/stack_smoke.py

Boundaries

This skill is a runtime router, not a maintainer guide. It does not cover release engineering, CI, packaging automation, or repo-specific development tasks. Keep the generated guidance self-contained inside this skill tree and use the sub-skills for workflow depth.