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.

脉冲神经网络（SNN）是一种受生物启发的神经网络模型，通过脉冲和脉冲列进行神经元之间的通信，与标准人工神经网络（ANN）不同。本文首先对比了 SNN 和脉冲神经 P 系统（SNPS）的结构、功能、优点和缺点，然后综述了 SNN 和 SNPS 形式化方法在机器学习和深度学习模型中的最新研究成果和应用。

脉冲神经膜系统学习模型和脉冲神经网络的调查

A survey on learning models of spiking neural membrane systems and  spiking neural networks

Spiking Neural P systems are a class of membrane computing models inspired
directly by biological neurons. Besides the theoretical progress made in this
new computational model, there are also numerous applications of P systems in
fields like formal verification, artificial intelligence, or cryptography.
Motivated by all the use cases of SN P systems, in this paper, we present a new
privacy-preserving protocol that enables a client to compute a linear function
using an SN P system hosted on a remote server. Our protocol allows the client
to use the server to evaluate functions of the form t_1k + t_2 without
revealing t_1, t_2 or k and without the server knowing the result. We also
present an SN P system to implement any linear function over natural numbers
and some security considerations of our protocol in the honest-but-curious
security model.

通过使用 Spiking Neural P 系统作为计算模型，本文提出了一种新的隐私保护协议，允许客户端在远程服务器上计算一个线性函数，并且不泄露任何输入和结果信息，同时保证服务器的安全性。