Abstract
Spiking neural networks (SNN) are a biologically inspired model of neural networks with certain brain-like properties. In the past few decades, this model has received increasing attention in computer science community, owing also to the successful phenomenon of deep learning. In SNN, communication between neurons takes place through the spikes and spike trains. This differentiates these models from the ``standard'' artificial neural networks (ANN) where the frequency of spikes is replaced by real-valued signals. Spiking neural P systems (SNPS) can be considered a branch of SNN based more on the principles of formal automata, with many variants developed within the framework of the membrane computing theory. In this paper, we first briefly compare structure and function, advantages and drawbacks of SNN and SNPS. A key part of the article is a survey of recent results and applications of machine learning and deep learning models of both SNN and SNPS formalisms.
Abstract (translated)
尖峰神经网络（SNN）是一种具有某些类脑性质的神经网络模型。在过去的几十年里，由于深度学习成功现象的影响，SNN在计算机科学领域得到了越来越多的关注。在SNN中，神经元之间的通信是通过尖峰和尖峰列车进行的。这一区别使得这些模型与“标准”人工神经网络（ANN）不同，ANN中的尖峰频率被替换为实数值信号。尖峰神经网络P系统（SNPS）可以被视为基于形式自动机原则的SNN分支，许多变体都是在膜计算理论框架内开发的。在本文中，我们首先简要比较了SNN和SNPS的结构和功能、优势和不足。文章的重点是对SNN和SNPS形式规范的机器学习和深度学习模型的最新结果和应用的调查。
URL
https://arxiv.org/abs/2403.18609
