Implementation of Arithmetic Operations With Time-Free Spiking Neural P Systems

Abstract

Spiking neural P systems (SN P systems) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes. In most applications of SN P systems, synchronization plays a key role which means the execution of a rule is completed in exactly one time unit (one step). However, such synchronization does not coincide with the biological fact: in biological nervous systems, the execution times of spiking rules cannot be known exactly. Therefore, a "realistic" system called time-free SN P systems were proposed, where the precise execution time of rules is removed. In this paper, we consider building arithmetical operation systems based on time-free SN P systems. Specifically, adder, subtracter, multiplier, and divider are constructed by using time-free SN P systems. The obtained systems always produce the same computation result independently from the execution time of the rules.

NES-REBS: A novel nuclear export signal prediction method using regular expressions and biochemical properties

“…It deserves for further research by checking if there exist some other properties that are better in distinguishing true NES from NES candidates. For example, biological networks [41][42][43] and machine learning methods 44,45 can be considered in this aspect.…”

An Approach for Detecting Fault Lines in a Small Current Grounding System using Fuzzy Reasoning Spiking Neural P Systems

“…9, where b, c, d, f and P 1 represent the fault line measure values of the zero sequence current phase, the zero sequence reactive power, the zero sequence admittance amplitude, the transient zero sequence current and fused fault measure, respectively. The rFRSNPS for fault line detection is described as follows: (4) syn = {(1, 12) , (2, 13), (3, 14), (4,15), (5,16), (5,17), (6,16), (7,17), (8,16), (8,17), (9,18), (10,18), (12,5), (13,6), (14,7), (15,8), (16,9), (17,10), (18,11),}; …”

“…Except for theoretical results [10,13], SN P systems have been widely used to solve various application problems, such as combinatorial and engineering optimization problems [30,38], signal recognition [2], arithmetic operations [17,21,28] and fuzzy knowledge representation [23]. Of a particular interest is the combination of SN P systems with fuzzy set theory, called fuzzy membrane computing [36], to solve fault diagnosis problems with respect to transformers [16,42], transmission lines [8,24,25], traction power supply systems of high-speed railways [39] and metro 522 H. Rong, M. Ge, G. Zhang, M. Zhu traction systems [10], in electric power systems.…”

A Cluster Splitting Technique by Hopfield Networks and P Systems on Simplices

“…For example, Zeng et al [37] study spiking neural P system with only one neuron simulating register machines, and other smaller weakly universal spiking neural P systems. There are also researches on asynchronous spiking neural P systems with promoters [35], spiking P systems to implement arithmetic operations [20], systems with fuzzy logic and firing mechanism [27], and applications with membrane algorithms [8].…”