Examples

Examples#

Runnable example scripts live in the examples directory. The top-level scripts are grouped by filename prefix:

  • 0xx- for spiking neural network examples

  • 1xx- for rate-based recurrent neural network examples

The directory also includes two tutorial-style example series:

  • examples/pp_prop/ for braintrace.pp_prop / ES_D_RTRL examples

  • examples/drtrl/ for braintrace.D_RTRL examples

Most scripts can be run directly. For example:

python examples/001-gif-snn-for-dms.py

Spiking Neural Networks#

  • 000-lif-snn-for-nmnist.py - Trains a recurrent LIF-delta SNN on the N-MNIST dataset, using framed event inputs from tonic and the shared OnlineTrainer implementation based on IODimVjpAlgorithm.

  • 001-gif-snn-for-dms.py - Trains a GIF recurrent SNN on a delayed matching-to-sample task generated by DMSDataset, using the shared OnlineTrainer based on IODimVjpAlgorithm for online learning.

  • 002-coba-ei-rsnn.py - Trains an excitatory/inhibitory recurrent SNN on an evidence-accumulation task, with configurable CUBA or COBA synapses and online learning via ES_D_RTRL or D_RTRL.

  • 003-snn-memory-and-speed-evaluation-all.py - Runs a synthetic benchmark over multiple sequence lengths and compares BPTT, ES_D_RTRL, and D_RTRL in terms of training time and memory usage.

  • 003-snn-memory-and-speed-evaluation-batched.py - Trains and benchmarks an SNN on the DVSGesture dataset with the online algorithms running in brainstate.mixin.Batching() mode, reporting accuracy, speed, and memory.

  • 003-snn-memory-and-speed-evaluation-vmap.py - Trains and benchmarks the same DVSGesture setup while keeping per-sample states under vmap, reporting accuracy, speed, and memory.

  • 004-feedforward-conv-snn.py - Builds a feedforward convolutional SNN with braintrace.nn.Conv2d, LayerNorm, IF neurons, and LeakyRateReadout. By default it trains on N-MNIST, and the script also includes SHD data loading plus both online and BPTT trainers.

Rate-Based Recurrent Neural Networks#

  • 100-gru-on-copying-task.py - Trains a GRU on the copying task and compares online learning with ParamDimVjpAlgorithm against BPTT.

  • 101-integrator-rnn.py - Trains a MiniGRU-based RNN to integrate a white-noise input signal and compares online D_RTRL with BPTT while plotting predictions and training losses.

Tutorial Series#

  • pp_prop/ - A tutorial-linear example suite for braintrace.pp_prop (ES_D_RTRL / IODimVjpAlgorithm). The series covers neuron-model variants, batching with vmap versus batched primitives, single-step versus multi-step VJPs, sparse/LoRA/convolutional operators, Poisson-MNIST classification, and decay_or_rank sweeps. See examples/pp_prop/README.md and docs/tutorials/pp_prop.md.

  • drtrl/ - A tutorial-linear example suite for braintrace.D_RTRL. The series covers integrator basics, batching modes, VJP variants, LoRA and convolutional operators, row-scan MNIST classification, toy character-level language modeling, and the fast_solve plus normalize_matrix_spectrum knobs. See examples/drtrl/README.md and docs/tutorials/drtrl.md.

Supporting Files#

  • README.md - Brief overview of the examples directory and its extra Python dependencies.

  • snn_models.py - Shared SNN model and trainer definitions used by the 000 and 001 examples.

  • utils.py - Shared command-line parser utilities used by the 003-* benchmark scripts.