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.

翻译：脉冲神经网络（SNN）是一种具有类脑特性的生物启发式神经网络模型。过去数十年来，得益于深度学习的成功浪潮，该模型在计算机科学领域受到日益广泛的关注。在SNN中，神经元之间的通信通过脉冲和脉冲序列实现，这使其区别于以实值信号替代脉冲频率的"标准"人工神经网络（ANN）。脉冲神经膜系统（SNPS）可视为更侧重于形式自动机原理的SNN分支，并在膜计算理论框架下衍生出诸多变体。本文首先简要比较SNN与SNPS在结构与功能、优势与局限性上的异同。文章核心部分系统综述了基于SNN与SNPS形式化体系的机器学习与深度学习模型的最新研究成果与应用进展。