A survey on learning models of spiking neural membrane systems and spiking neural networks
It provides a comprehensive overview for researchers interested in biologically inspired neural models, but it is incremental as it primarily reviews existing work.
This paper surveys recent results and applications of machine learning and deep learning models for spiking neural networks (SNN) and spiking neural P systems (SNPS), comparing their structures, functions, advantages, and drawbacks.
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.