Brain-Inspired Reservoir Computing with Dynamic Memristors for Audio-Visual Emotion Recognition

The rapid growth of edge artificial intelligence (AI) and the escalating computational demands of multimodal perception systems accentuate the bottlenecks of von Neumann architecture and the high costs of training. Therefore, to streamline system construction, reduce energy consumption, and improve the processing efficiency of artificial perception systems, the advancement of emerging device technologies and next-generation computing architecture is urgently required. Here, we present a brain-inspired reservoir computing (RC) framework employing dynamic memristors with the Ag/WO<i>x</i>/ITO structure, enabling efficient audio-visual emotion recognition. The developed WO<i>x</i>-based memristor demonstrates key biological synaptic functionalities, including short-term plasticity, paired-pulse facilitation/depression, and nonlinear temporal dynamics, enabling its use as an effective physical reservoir. By exploiting the device's transient response to electrical pulses and its nonlinear mapping capability, complex spatiotemporal signals from auditory and visual modalities are transformed into separable high-dimensional reservoir states through simple 4-bit pulse sequences. The implemented dual-modal RC system achieves remarkable classification accuracies of 95.46% for speech-based emotion recognition and 91.08% for facial expression recognition, with an energy cost of only ∼3.9 nJ per pulse operation. This work provides a compelling hardware paradigm for low-power, real-time audio-visual perception on edge devices, paving the way for advanced neuromorphic computing.