Spiking-Timing-Dependent Plasticity Convolutional Spiking Neural Network for Efficient Radar-Based Gesture Recognition

An innovative architecture, termed Spike-Timing-Dependent Plasticity Convolutional Spiking Neural Network (STDP-CSNN), is proposed for efficient radar-based gesture recognition in this paper. Radar range-Doppler image data are encoded into spike sequences with CSNN, and the leaky integrate-and-fire...

Full description

Saved in:
Bibliographic Details
Published in2023 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) pp. 617 - 620
Main Authors Wu, Yifan, Wu, Li, Xiao, Zelong, Hu, Taiyang
Format Conference Proceeding
LanguageEnglish
Published IEEE 03.11.2023
Subjects
component
Computational complexity
Computer architecture
Convolution
Gesture recognition
Power demand
SNN
STDP
Training
Unsupervised learning
Online AccessGet full text

Cover

More Information
Summary:An innovative architecture, termed Spike-Timing-Dependent Plasticity Convolutional Spiking Neural Network (STDP-CSNN), is proposed for efficient radar-based gesture recognition in this paper. Radar range-Doppler image data are encoded into spike sequences with CSNN, and the leaky integrate-and-fire (LIF) model is employed as a neuron in the network nodes. This design enables efficient accumulation and transmission of spike signals, resulting in a significant reduction in network power consumption. Furthermore, the unsupervised learning algorithm of STDP is employed to facilitate feature extraction in the established CSNN, ensuring low computational complexity. The experimental results demonstrate that the proposed STDP-CSNN architecture achieves an impressive recognition accuracy of 92.72%, while concurrently addressing the crucial requirements of low power consumption and computational simplicity.
DOI:10.1109/ICICML60161.2023.10424838