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name: core-neurons description: "Build and debug snnTorch neuron, recurrent layer, state lifecycle, and batchnorm-through-time workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

core-neurons

Use this sub-skill when the task is to construct, combine, or debug snnTorch neuron and neuron-layer workflows.

Route here for

• Core neuron construction and calls for Leaky, Synaptic, RLeaky, RSynaptic, Lapicque, Alpha, and DeltaLeaky.
• Time-parallel or state-only neuron layers: LinearLeaky, StateLeaky, and LeakyParallel.
• Recurrent and LSTM-like spiking layers: SConv2dLSTM, SLSTM, and AssociativeLeaky.
• Hidden-state lifecycle choices: manual state tuples, init_hidden=True, reset_mem, class-level reset_hidden/detach_hidden, and snntorch.utils.reset.
• Reset semantics, learnable decay/threshold flags, recurrent wiring choices, BatchNormTT1d/BatchNormTT2d, and GradedSpikes.

Do not handle here

• Spike encodings, surrogate-gradient selection depth, loss functions, metrics, STDP, quantization recipes, or full training-loop design: route to encoding-training.
• NIR export/import or NIR graph round trips: route to nir-interoperability.
• Spike raster/count/trace/animation plotting: route to plotting.
• Legacy neuromorphic dataset wrappers or dataset downloads: route to spikevision.

Operating workflow

1. Identify whether the user needs a stepwise neuron cell, a time-major sequence layer, a recurrent cell, or a helper layer.
2. Check the exact constructor, state tuple, and return contract in API reference.
3. Pick the state-management pattern in workflows: manual states, init_hidden=True, stateless time-major layers, or LeakyParallel.
4. Before changing model logic, triage state-count, reset, learnable-parameter, device, and shape failures with troubleshooting.
5. For a safe local smoke check, run one of the bundled scripts below; they use synthetic tensors only and require no downloads.

Bundled scripts

• scripts/leakyparallel_forward_smoke.py: synthetic LeakyParallel forward/gradient smoke, including diagonal recurrent-gradient behavior.
• scripts/leakyparallel_train_smoke.py: tiny synthetic LeakyParallel optimization smoke adapted from the example training pattern without external data.
• scripts/mixed_state_chain_smoke.py: difficult mixed Leaky + LinearLeaky + StateLeaky state-reset case, with BNTT and GradedSpikes sanity checks.
• scripts/associative_leaky_smoke.py: AssociativeLeaky.from_num_spiking_neurons q-projection toggle plus chunked-batch gradient equivalence.
• scripts/spiking_lstm_smoke.py: SLSTM and SConv2dLSTM state-tuple and shape smoke.

Start with workflows for recipes and API reference for argument-level constraints.