Stable Neural Signal Recording Processed by Memristor‐Based Reservoir Computing System

As brain–machine interfaces (BMIs) and neural recording technologies evolve, there is an increasing demand for edge computing systems capable of processing large amounts of neural data in real‐time to alleviate the data transmission challenges and improve BMI performance. In this work, we propose a memristor‐based reservoir computing (RC) system that leverages the short‐term memory dynamics of memristive devices to process recorded neural signals. We validate the proposed system on a behavioral state classification task using neural spike recordings from a mouse during free movement. The system achieves robust classification performance over a 2‐week period and demonstrates resilience to device‐to‐device variations and limited training data. The proposed system further enabled ablation analysis to identify the dominate neurons responsible for particular actions. These results demonstrate the effectiveness of memristor‐based RC systems as promising solutions for energy‐efficient, real‐time neural signal processing in future BMI systems.