The comprehensive bilingual (EN/中文) hub for Spiking Neural Networks — 340+ papers, models, neuromorphic hardware, datasets, tools & research groups.

Minimal PyTorch examples for the four-stage structural evolution from ANN to event-driven SNN: Stage 0 (baseline ANN) → Stage 1 (binarization) → Stage 2 (temporal expansion) → Stage 3 (temporal accumulation) → Stage 4 (reset & sparsity control).

Official code for "Attacking the Spike: On the Security of Spiking Neural Networks to Adversarial Examples" (Neurocomputing 2025). Implements the MDSE adversarial attack for SNNs, CNNs, and Vision Transformers.

At matched capacity, matching a spiking neuron to the signal physics beats neuron-type heterogeneity. Pure-SNN experiments on radar micro-Doppler (DIAT-µSAT) and audio (SHD) with a cross-domain double dissociation.

Neuromorphic benchmark suite: SHD, SSC, N-MNIST, DVS Gesture, GSC KWS — reproducible training scripts for Catalyst processors

针对《A Brain-inspired Embodied Intelligence for Fluid and Fast Reflexive Robotics Control》论文的解读

Device-to-algorithm neuromorphic: train spiking neural nets in snnTorch, then deploy on a simulated memristor crossbar with measured SnS2 device non-idealities.