Leveraging Homeostatic Plasticity to Enable Anomaly Detection in Spiking Neural Networks

Anomaly detection systems are crucial for identifying deviations from normal behavior in various domains, ranging from cybersecurity to industrial system monitoring. Traditional approaches often rely on predefined rules or supervised learning algorithms, which may struggle to adapt to complex and evolving patterns. As a result, deep learning techniques have been employed to address these challenges, but they usually require significant computational resources and memory for training and inference. On the contrary, Spiking Neural Networks (SNNs), inspired by the brain neural architecture, excel in effectively capturing complex temporal patterns present in real-world data through unsupervised learning, potentially leading to more robust performance while reducing energy consumption. In this paper, we employ the homeostatic plasticity characteristic that governs the synapses behaviour in SNN to implement an anomaly detection system designed for processing raw vibration data. Initially, we generate spike trains to represent the input data. These are then fed into a Balanced Spiking Neural Network (BSNN) to detect suspicious anomalies. The proposed SNN learning algorithm dynamically adjusts synaptic weights based on the precise timing of spikes between neurons, and introduces a combination of Spike Timing Dependent Plasticity (STDP) and reverse STDP techniques. Experimental results conducted on a public available vibration dataset demonstrate the effectiveness of the proposed approach in accurately identifying anomalies while maintaining low false positive rates.