A formal framework for spiking neural P systems

Abstract

Spiking neural P systems are a class of distributed parallel computing models, inspired by the way in which neurons process information and communicate with each other by means of spikes. In 2007, Freund and Verlan developed a formal framework for P systems to capture most of the essential features of P systems and to define their functioning in a formal way. In this work, we present an extension of the formal framework related to spiking neural P systems by considering the applicability of each rule to be controlled by specific conditions on the current contents of the cells. The main objective of this extension is to also capture spiking neural P systems in the formal framework. Another goal of our extension is to incorporate the notions of input and output. Finally, we also show that in the case of spiking neural P systems, the rules have a rather simple form and in that way spiking neural P systems correspond to vector addition systems where the application of rules is controlled by semi-linear sets.

A Layered Spiking Neural System for Classification Problems

“…These variants have solved many theory and applications problems. [28][29][30] As well, SN P systems have been successfully applied in various real-world scenarios, including combinatorial optimisation, 31,32 fault diagnosis 33 and arithmetic calculator. 34 The capabilities of SN P systems for addressing classification problems have also been investigated (see e.g.…”

Snapse: A Visual Tool for Spiking Neural P Systems

“…There are also works that focus on simulating SN P systems, as they are parallel in nature, in GPUs such as [9,10] and more recently in [11][12][13]. Much theoretical work has been done on SN P systems, e.g., their normal forms [14][15][16], formal representations [17][18][19], and their relations to classical models of computation [20][21][22][23][24][25] with a short and recent survey in [26]. After much theoretical work, more recently the work to apply SN P systems to real-world problems becomes even more active, with some early works on image processing e.g., [27] and more recently in [28], use for cryptography [29][30][31], use of evolutionary algorithms to design SN P systems [32][33][34], in pattern recognition [35,36], computational biology [37], with a recent survey in [38].…”