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

Sicheng Wan1,2, Yibo Wang1, Honglei Chen2

  • 1Guangdong Engineering Research Center of Optoelectronic Functional Materials and Devices, School of Electronic Science and Engineering (School of Microelectronics), South China Normal University, Foshan 528225, China.

ACS Applied Materials & Interfaces
|January 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel brain-inspired computing framework using dynamic memristors for efficient audio-visual emotion recognition. The system achieves high accuracy with low energy consumption, advancing edge artificial intelligence (AI) capabilities.

Keywords:
audio-visual perceptionemotion recognitionneuromorphic deviceoxide-memristorreservoir computing

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Area of Science:

  • Neuromorphic Engineering
  • Artificial Intelligence
  • Materials Science

Background:

  • Edge AI and multimodal perception systems face computational bottlenecks and high training costs due to von Neumann architecture limitations.
  • Emerging device technologies and novel computing architectures are crucial for improving efficiency and reducing energy consumption in artificial perception.

Purpose of the Study:

  • To develop a brain-inspired reservoir computing (RC) framework using dynamic memristors for efficient audio-visual emotion recognition.
  • To address the limitations of current architectures for edge AI applications.

Main Methods:

  • Utilized Ag/WOx/ITO dynamic memristors exhibiting biological synaptic functionalities as physical reservoirs.
  • Employed the memristor's transient response and nonlinear mapping to transform spatiotemporal audio-visual signals into separable high-dimensional reservoir states using 4-bit pulse sequences.

Main Results:

  • Achieved 95.46% accuracy for speech-based emotion recognition and 91.08% for facial expression recognition.
  • Demonstrated ultra-low energy consumption of approximately 3.9 nJ per pulse operation.

Conclusions:

  • The developed memristor-based RC framework offers a viable hardware paradigm for low-power, real-time audio-visual perception on edge devices.
  • This approach paves the way for advanced neuromorphic computing and more efficient artificial perception systems.