Danger-OS: Spiking Neural Danger Theory — Affective Neuromodulatory Arbitration for Real-Time Behavioural Anomaly Detection

We present Danger-OS, the first application of the Maya affective Spiking Neural Network architecture to a defence and security context. Four Leaky Integrate-and-Fire neurons — Bhaya (fear, τ=3), Vairagya (wisdom, τ=20), Shraddha (trust, τ=10), and Spanda (aliveness, τ=5) — continuously read live system telemetry and produce behavioural anomaly detection decisions at a 500 ms tick cadence. Across eight experimental scenarios totalling 5,710 ticks of continuous operation, we demonstrate: (1) the Bhaya Quiescence Law holds in defence context — aggregate terminal-action rate of 0.315%; (2) reproducibility across independent sessions; (3) adversarial robustness against direct voltage injection; (4) autonomous…