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name: encoding-training description: "Route spike encoding, surrogate gradients, training helpers, losses, monitors, quantization, STDP, and backprop-wrapper questions." metadata: disco-role: operating disable-model-invocation: true license: MIT

encoding-training

Use this sub-skill for workflows that start with raw tensors or labels and end with spike-coded data, a training step, or output/gradient inspection.

Ask this sub-skill about

• spikegen encodings: rate, latency, delta, and target coding
• surrogate gradients for neuron spike_grad
• loss and accuracy helpers in snntorch.functional
• utils.reset, data_subset, and valid_split
• backprop wrappers: BPTT, TBPTT, RTRL
• monitors for spikes, inputs, attributes, and gradients
• functional.quant.state_quant
• functional.stdp_learner.STDPLearner

Route elsewhere

• neuron definitions and reset semantics in detail -> core-neurons
• NIR export/import -> nir-interoperability
• raster plots, animations, and spike histograms -> plotting
• legacy spikevision datasets -> spikevision

Start here

1. references/workflows.md
2. references/api-reference.md
3. references/troubleshooting.md

Bundled smoke helpers

• scripts/spike_encoding_smoke.py
• scripts/synthetic_bptt_smoke.py
• scripts/shape_mismatch_diagnostic.py
• scripts/stdp_smoke.py

Import reminders

• Use import snntorch.functional as SF for accuracy, losses, regularization, and probes.
• Import quantization and STDP helpers from their submodules: from snntorch.functional import quant from snntorch.functional.stdp_learner import STDPLearner
• There is no standalone SF.metrics namespace in this release; use SF.accuracy_rate and SF.accuracy_temporal.
• backprop is a compatibility layer and emits a deprecation warning.
• Call utils.reset(net) between sequences unless a wrapper already does it.
• STDPLearner.reset() is not reliable in this release; create a fresh learner when you need a clean STDP episode.