Second-order information bottleneck based spiking neural networks for sEMG recognition

DOI10.1016/j.ins.2021.11.065OpenAlexW3217337493MaRDI QIDQ6149522

Anguo Zhang, Junyi Wu, Yueming Gao, Yuzhen Niu, Zhipeng Gao

Publication date: 5 February 2024

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2021.11.065

zbMATH Keywords

spiking neural networksecond-order information bottlenecksurface electromyography (sEMG) recognition

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Biomedical imaging and signal processing (92C55)

Cites Work

• A remark on the error-backpropagation learning algorithm for spiking neural networks
• Error-backpropagation in temporally encoded networks of spiking neurons
• Reinforcement Learning Through Modulation of Spike-Timing-Dependent Synaptic Plasticity
• Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations
• A Population Study of Integrate-and-Fire-or-Burst Neurons
• A Hidden Markov, Multivariate Autoregressive (HMM-mAR) Network Framework for Analysis of Surface EMG (sEMG) Data
• The Deterministic Information Bottleneck
• Reinforcement Learning, Spike-Time-Dependent Plasticity, and the BCM Rule
• Learning Beyond Finite Memory in Recurrent Networks of Spiking Neurons

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