Skip to content

Examples Gallery

The examples/ directory contains production-quality scripts that demonstrate core btorch patterns. This page annotates each example and links to the relevant tests and API docs.

rsnn.py — Minimal RSNN

A self-contained demonstration of a recurrent spiking neural network using GLIF3, AlphaPSC, and RecurrentNN. It includes both a simulation loop (with raster-plot generation) and a dummy training loop.

Key patterns: - functional.init_net_state and functional.reset_net - environ.context(dt=1.0) - update_state_names for state recording - plot_raster and plot_neuron_traces from btorch.visualisation

See also: - Tutorial 1: Building an RSNN - Tutorial 2: Training an SNN

rsnn_brain.py — Brain-Environment Interaction

Extends the basic RSNN with brain-environment interaction networks. Demonstrates NeuronEmbedMapLayer and DetectionWindow for sensory-motor tasks.

Key patterns: - Embedding maps between neuron spaces - Detection windows for spike-based event detection - Multi-module composition beyond RecurrentNN

fmnist.py — Fashion-MNIST Training (Plain PyTorch)

A full training pipeline for Fashion-MNIST classification using a GLIF3-based RSNN with sparse recurrent connectivity and voltage regularization.

Key patterns: - SparseConn for sparse recurrent weights - AlphaPSCBilleh synapse with heterogeneous time constants - VoltageRegularizer for membrane-voltage regularization - Manual training loop with reset_net per batch - Checkpointing memories_rv alongside state_dict()

See also: - Tutorial 2: Training an SNN - API: btorch.models.regularizer

fmnist_lightning.py — PyTorch Lightning Integration

The same Fashion-MNIST model factored into a PyTorch Lightning LightningModule. Shows how btorch state management fits into the Lightning training lifecycle.

Key patterns: - Lightning training_step with reset_net - scale_net / unscale_net for neuron-parameter scaling - Validation loop with state recording

delayed_synapse_demo.py — Synaptic Delays

Demonstrates synaptic delays using SpikeHistory, DelayedPSC, and HeterSynapsePSC.

Key patterns: - SpikeHistory as a rolling spike buffer - DelayedPSC for adding scalar delays to any PSC model - HeterSynapsePSC(..., max_delay_steps=...) for delay + receptor combinations - expand_conn_for_delays for delay-aware connection matrices

See also: - API: btorch.models.history and btorch.models.synapse - Tests: tests/connectome/test_delay_expansion.py

Tests as Examples

Many tests/ files contain concise, validated usage patterns that are ideal for documentation:

  • tests/models/test_mem_load_save.py — Checkpointing and state restoration
  • tests/models/test_compile.py — torch.compile with dt context
  • tests/utils/test_conf.py — OmegaConf patterns
  • tests/visualisation/*.py — Nearly every plotting function