LogicSNN: A Unified Spiking Neural Networks Logical Operation Paradigm

被引：10

作者：

Mo, Lingfei ^{[1
]}

Wang, Minghao ^{[1
]}

机构：

[1] Southeast Univ, Sch Instrument Sci & Engn, FutureX Lab, Nanjing 210096, Peoples R China

来源：

ELECTRONICS | 2021年 / 10卷 / 17期

关键词：

spiking neural networks; logical operation; spike-timing-dependent plasticity;

D O I：

10.3390/electronics10172123

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

LogicSNN, a unified spiking neural networks (SNN) logical operation paradigm is proposed in this paper. First, we define the logical variables under the semantics of SNN. Then, we design the network structure of this paradigm and use spike-timing-dependent plasticity for training. According to this paradigm, six kinds of basic SNN binary logical operation modules and three kinds of combined logical networks based on these basic modules are implemented. Through these experiments, the rationality, cascading characteristics and the potential of building large-scale network of this paradigm are verified. This study fills in the blanks of the logical operation of SNN and provides a possible way to realize more complex machine learning capabilities.

引用

页数：19

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