SNNV ==== .. raw:: html

SNNV

Sound set-based robustness verification for first-to-fire (F2F) Leaky Integrate-and-Fire spiking neural networks. The spiking star set separates temporal spike patterns from affine propagation, enabling star-set reachability without exhaustive latency enumeration.

Python 3.10+ PyTorch / snnTorch Star-set reachability HiGHS LP MNIST / F-MNIST / CIFAR-10 / GTSRB
The Core Idea
Why SNN verification is hard, and the spiking-star insight that makes it tractable.
How Spike Star Works
Generalized star sets, F2F encoding, spike patterns, effective synapses, affine propagation, adaptive latency splitting.
Interactive Playground
Drag the input star (box or zonotope); watch the latency cells, membrane envelopes, score enclosure, and robustness verdict update live.
Benchmark Results
Per-case browser over MNIST / Fashion-MNIST / CIFAR-10 / GTSRB, with SMT-baseline comparison.
Getting Started
Install with uv and run the first-to-fire reachability demo.
.. toctree:: :hidden: :maxdepth: 1 core_idea spike_star playground results setup