Activation-Scaled ANN-to-SNN Conversion with SNN Guardrail: A Unified Framework for AI Interpretability, Hallucination Detection, and Real-Time Adversarial Defense

I present a unified framework that extends ANN-to-SNN conversion beyond efficiency optimization to enable novel AI interpretability analysis and real-time adversarial defense. My approach uses Spiking Neural Networks as "computational microscopes" to analyze black-box AI models. **v4 Updates:**- NEW: SNN Guardrail for real-time AI safety with **100% jailbreak detection rate** (8/8 attack types)- NEW: Scaling Law Discovery - TTFS sensitivity increases with model size (GPT-2: +3.1, TinyLlama 1.1B: +4.2)- NEW: TinyLlama (1.1B params) validation- NEW: Neural instability detection (jailbreak attacks cause +10 to +19σ TTFS deviation) **Previous Results (v1-v3):**- Universal threshold formula: θ = 2.0 × max(activation)- 100% accuracy preservation with hippocampal hybrid architecture- GPT-2 attention TTFS analysis: +3.1 increase for meaningless inputs- Hallucination detection: AUC 0.75 with ensemble classifier- ViT-Base (86M params) validation with CIFAR-100 Key insight: "Measure the AI's brainwaves, and block it when it's about to lie." Code: https://github.com/hafufu-stack/temporal-coding-simulation/tree/main/ann-to-snn-converter