Sam: a unified self-adaptive multicompartmental spiking neuron model for learning with working memory

HIGHLIGHTS

  • who: Shuangming Yang and Tao Lei from the School of Electrical and Information, Tianjin University, Tianjin, China, College of Science and have published the research work: SAM: A Unified Self-Adaptive Multicompartmental Spiking Neuron Model for Learning With Working Memory, in the Journal: (JOURNAL)
  • what: The main contributions of this work are as follows: 1) A novel neuronal model, SAM, is introduced for efficient learning in SNN architectures. The authors propose a sparse, SAM-based recurrent SNN architecture, along with spike-driven learning algorithms in supervised and meta-learning frameworks. The authors demonstrate that SAM . . .

     

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