Exploring temporal information dynamics in Spiking Neural Networks: Fast Temporal Efficient Training.

Journal: Journal of neuroscience methods
PMID: 40015573

Abstract

BACKGROUND: Spiking Neural Networks (SNNs) hold significant potential in brain simulation and temporal data processing. While recent research has focused on developing neuron models and leveraging temporal dynamics to enhance performance, there is a lack of explicit studies on neuromorphic datasets. This research aims to address this question by exploring temporal information dynamics in SNNs.

Authors

  • Changjiang Han
    School of Railway Intelligent Engineering, Dalian Jiaotong University, Dalian, 116000, Liaoning, China.
  • Li-Juan Liu
    School of Railway Intelligent Engineering, Dalian Jiaotong University, Dalian, 116000, Liaoning, China. Electronic address: liulj@djtu.edu.cn.
  • Hamid Reza Karimi
    Department of Engineering, Faculty of Technology and Science, University of Agder, N-4898 Grimstad, Norway.

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