Feasibility study on the application of a spiking neural network in myoelectric control systems
In recent years, the effectiveness of a spiking neural network (SNN) for Electromyography (EMG) pattern recognition has been validated, but there is a lack of comprehensive consideration of the problems of heavy training burden, poor robustness, and high energy consumption in the application of actu...
Main Authors: | Antong Sun, Xiang Chen, Mengjuan Xu, Xu Zhang, Xun Chen |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2023-06-01
|
Series: | Frontiers in Neuroscience |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1174760/full |
Similar Items
-
MAP-SNN: Mapping spike activities with multiplicity, adaptability, and plasticity into bio-plausible spiking neural networks
by: Chengting Yu, et al.
Published: (2022-09-01) -
Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing
by: Moshe Bensimon, et al.
Published: (2021-02-01) -
Optimization of Spiking Neural Networks Based on Binary Streamed Rate Coding
by: Ali A. Al-Hamid, et al.
Published: (2020-09-01) -
Reduce the User Burden of Multiuser Myoelectric Interface via Few-Shot Domain Adaptation
by: Bo Xue, et al.
Published: (2023-01-01) -
Editorial: Spiking Neural Network Learning, Benchmarking, Programming and Executing
by: Guoqi Li, et al.
Published: (2020-04-01)