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增强的表示学习与时代编码在稀疏的尖端神经网络

Adrien Fois1, Bernard Girau1

  • 1Université de Lorraine, Centre National de la Recherche Scientifique, Laboratoire lorrain de Recherche en Informatique et ses Applications, Nancy, France.

Frontiers in computational neuroscience
|December 11, 2023
PubMed
概括

本研究介绍了用于尖端神经网络 (SNN) 的权重时间编码表示学习 (W-TCRL). W-TCRL使用时代编码来实现高效的表示学习,显著减少重建错误并改善稀疏性.

关键词:
延迟编码的延迟编码.代表性学习学习学习稀缺性是一种稀缺性.峰值时间依赖的可塑性刺激神经网络的神经网络.时间代码时间代码.没有监督的学习学习.视觉表现是一种视觉表现.

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