Neuromorphic Continual Learning for Sequential Deployment of Nuclear Plant Monitoring Systems
Anomaly detection in nuclear industrial control systems (ICS) requires continuous, energy-efficient monitoring across multiple subsystems that are often deployed at different stages of plant commissioning. When a conventional neural network is sequentially trained to monitor new subsystems, it catastrophically forgets previously learned anomaly patterns, a safety-critical failure mode. We present the first spiking neural network (SNN)-based anomaly detection system with continual learning for nuclear ICS, addressing both challenges simultaneously. Our approach introduces spike-encoded asynchronous sensor fusion, a delta-based encoding that converts heterogeneous sensor streams into sparse...
| Authors | Samrendra Roy, Sajedul Talukder, Syed Bahauddin Alam |
|---|---|
| Primary category | cs.NE |
| Published | 13 April 2026 |
| Last updated at source | 13 April 2026 |
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