Performing Balanced Ternary Logic and Arithmetic Operations with Spiking Neural P Systems with Anti-Spikes

2012 ◽  
Vol 505 ◽  
pp. 378-385 ◽  
Author(s):  
Xian Wu Peng ◽  
Xiao Ping Fan ◽  
Jian Xun Liu
Keyword(s):  
Logic Gates ◽  
P Systems ◽  
P System ◽  
Ternary Logic ◽  
Arithmetic Operations ◽  
Spiking Neural P Systems ◽  
Addition And Subtraction ◽  
Distributed And Parallel Computing ◽  
Computing Models ◽  
Natural Way

Spiking neural P systems are a class of distributed and parallel computing models inspired by P systems and spiking neural networks.Spiking neural P system with anti-spikes can encode the balanced ternary three digits in a natural way using three states called anti-spikes, no-input and spikes. In this paper we use this variant of SN P system to simulate universal balanced ternary logic gates including AND,OR and NOT gate and to perform some basic balanced ternary arithmetic operations like addition and subtraction on balanced ternary integers. This paper provides an applicational answer to an open problem formulated by L.Pan and Gh. Păun.

COMPUTABILITY OF SPIKING NEURAL P SYSTEMS WITH ANTI-SPIKES

2012 ◽  
Vol 08 (03) ◽  
pp. 283-295 ◽  
Author(s):  
VENKATA PADMAVATI METTA ◽  
KAMALA KRITHIVASAN ◽  
DEEPAK GARG
Keyword(s):  
Arithmetic Operation ◽  
P Systems ◽  
Spiking Neural P Systems ◽  
Addition And Subtraction ◽  
Natural Way

Spiking neural P systems with anti-spikes (for short, SN PA systems) can encode the binary digits in a natural way using two types of objects called anti-spikes and spikes. In this paper, we use SN PA systems to perform the arithmetic operation like 2's complement, addition and subtraction of binary numbers. They are also used to simulate NAND and NOR gates.

scholarly journals A Complete Arithmetic Calculator Constructed from Spiking Neural P Systems and its Application to Information Fusion

2020 ◽  
Vol 31 (01) ◽  
pp. 2050055 ◽  
Author(s):  
Gexiang Zhang ◽  
Haina Rong ◽  
Prithwineel Paul ◽  
Yangyang He ◽  
Ferrante Neri ◽  
...  
Keyword(s):  
Information Fusion ◽  
Arithmetic Operation ◽  
P Systems ◽  
Arithmetic Operations ◽  
Spiking Neural P Systems ◽  
Probability Assignment ◽  
Basic Probability Assignment ◽  
Basic Probability ◽  
Addition And Subtraction

Several variants of spiking neural P systems (SNPS) have been presented in the literature to perform arithmetic operations. However, each of these variants was designed only for one specific arithmetic operation. In this paper, a complete arithmetic calculator implemented by SNPS is proposed. An application of the proposed calculator to information fusion is also proposed. The information fusion is implemented by integrating the following three elements: (1) an addition and subtraction SNPS already reported in the literature; (2) a modified multiplication and division SNPS; (3) a novel storage SNPS, i.e. a method based on SNPS is introduced to calculate basic probability assignment of an event. This is the first attempt to apply arithmetic operation SNPS to fuse multiple information. The effectiveness of the presented general arithmetic SNPS calculator is verified by means of several examples.

scholarly journals Implementation of Arithmetic Operations by SN P Systems with Communication on Request

2018 ◽  
Vol 13 (3) ◽  
pp. 353-364
Author(s):  
Yun Jiang ◽  
Yuan Kong ◽  
Chaoping Zhu
Keyword(s):  
Parallel Computing ◽  
P Systems ◽  
Arithmetic Operations ◽  
Spiking Neural P Systems ◽  
Grammar Systems ◽  
Distributed And Parallel Computing ◽  
The Way

Spiking neural P systems (SN P systems, for short) are a class of distributed and parallel computing devices inspired from the way neurons communicate by means of spikes. In most of the SN P systems investigated so far, the system communicates on command, and the application of evolution rules depends on the contents of a neuron. However, inspired from the parallel-cooperating grammar systems, it is natural to consider the opposite strategy: the system communicates on request, which means spikes are requested from neighboring neurons, depending on the contents of the neuron. Therefore, SN P systems with communication on request were proposed, where the spikes should be moved from a neuron to another one when the receiving neuron requests that. In this paper, we consider implementing arithmetical operations by means of SN P systems with communication on request. Specifically, adder, subtracter and multiplier are constructed by using SN P systems with communication on request.

Spiking Neural P Systems with Thresholds

2014 ◽  
Vol 26 (7) ◽  
pp. 1340-1361 ◽  
Author(s):  
Xiangxiang Zeng ◽  
Xingyi Zhang ◽  
Tao Song ◽  
Linqiang Pan
Keyword(s):  
Parallel Computing ◽  
Open Problem ◽  
Spiking Neurons ◽  
P Systems ◽  
Spiking Neural P Systems ◽  
New Class ◽  
Semilinear Sets ◽  
The Family ◽  
Distributed And Parallel Computing ◽  
Computing Models

Spiking neural P systems with weights are a new class of distributed and parallel computing models inspired by spiking neurons. In such models, a neuron fires when its potential equals a given value (called a threshold). In this work, spiking neural P systems with thresholds (SNPT systems) are introduced, where a neuron fires not only when its potential equals the threshold but also when its potential is higher than the threshold. Two types of SNPT systems are investigated. In the first one, we consider that the firing of a neuron consumes part of the potential (the amount of potential consumed depends on the rule to be applied). In the second one, once a neuron fires, its potential vanishes (i.e., it is reset to zero). The computation power of the two types of SNPT systems is investigated. We prove that the systems of the former type can compute all Turing computable sets of numbers and the systems of the latter type characterize the family of semilinear sets of numbers. The results show that the firing mechanism of neurons has a crucial influence on the computation power of the SNPT systems, which also answers an open problem formulated in Wang, Hoogeboom, Pan, Păun, and Pérez-Jiménez ( 2010 ).

ON STRING LANGUAGES GENERATED BY SPIKING NEURAL P SYSTEMS WITH ANTI-SPIKES

2011 ◽  
Vol 22 (01) ◽  
pp. 15-27 ◽  
Author(s):  
KAMALA KRITHIVASAN ◽  
VENKATA PADMAVATI METTA ◽  
DEEPAK GARG
Keyword(s):  
P Systems ◽  
P System ◽  
Spiking Neural P System ◽  
Spiking Neural P Systems ◽  
Natural Way

An Spiking Neural P system with anti-spikes uses two types of objects called spikes and anti-spikes which can encode binary digits in a natural way. The step when system emits a spike or an anti-spike is associated with symbol 1 and 0, respectively. Here we consider these computing devices as language generators. They allow non-determinism between the rules ac → a and ac → ā, c ϵ ℕ, thus help to generate languages which cannot be generated using simple SN P systems.

scholarly journals Novel Numerical Spiking Neural P Systems with a Variable Consumption Strategy

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 549
Author(s):  
Xiu Yin ◽  
Xiyu Liu ◽  
Minghe Sun ◽  
Qianqian Ren
Keyword(s):  
Production Functions ◽  
P Systems ◽  
P System ◽  
Computing Device ◽  
Spiking Neural P Systems ◽  
Distributed Parallel Computing ◽  
Production Values ◽  
Novel Variant ◽  
Turing Universality ◽  
Variable Consumption

A novel variant of NSN P systems, called numerical spiking neural P systems with a variable consumption strategy (NSNVC P systems), is proposed. Like the spiking rules consuming spikes in spiking neural P systems, NSNVC P systems introduce a variable consumption strategy by modifying the form of the production functions used in NSN P systems. Similar to the delay feature of the spiking rules, NSNVC P systems introduce a postponement feature into the production functions. The execution of the production functions in NSNVC P systems is controlled by two, i.e., polarization and threshold, conditions. Multiple synaptic channels are used to transmit the charges and the production values in NSNVC P systems. The proposed NSNVC P systems are a type of distributed parallel computing models with a directed graphical structure. The Turing universality of the proposed NSNVC P systems is proved as number generating/accepting devices. Detailed descriptions are provided for NSNVC P systems as number generating/accepting devices. In addition, a universal NSNVC P system with 66 neurons is constructed as a function computing device.

scholarly journals A Grid-Density Based Algorithm by Weighted Spiking Neural P Systems with Anti-Spikes and Astrocytes in Spatial Cluster Analysis

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1132
Author(s):  
Deting Kong ◽  
Yuan Wang ◽  
Xinyan Wu ◽  
Xiyu Liu ◽  
Jianhua Qu ◽  
...  
Keyword(s):  
Dimensional Space ◽  
P Systems ◽  
High Dimensional ◽  
P System ◽  
Inhibitory Influence ◽  
Spiking Neural P Systems ◽  
Clustering Approach ◽  
Spatial Cluster Analysis ◽  
Effectiveness And Efficiency ◽  
Real World Datasets

In this paper, we propose a novel clustering approach based on P systems and grid- density strategy. We present grid-density based approach for clustering high dimensional data, which first projects the data patterns on a two-dimensional space to overcome the curse of dimensionality problem. Then, through meshing the plane with grid lines and deleting sparse grids, clusters are found out. In particular, we present weighted spiking neural P systems with anti-spikes and astrocyte (WSNPA2 in short) to implement grid-density based approach in parallel. Each neuron in weighted SN P system contains a spike, which can be expressed by a computable real number. Spikes and anti-spikes are inspired by neurons communicating through excitatory and inhibitory impulses. Astrocytes have excitatory and inhibitory influence on synapses. Experimental results on multiple real-world datasets demonstrate the effectiveness and efficiency of our approach.

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

2015 ◽  
Vol 14 (6) ◽  
pp. 617-624 ◽  
Author(s):  
Xiangrong Liu ◽  
Ziming Li ◽  
Juan Liu ◽  
Logan Liu ◽  
Xiangxiang Zeng
Keyword(s):  
P Systems ◽  
Arithmetic Operations ◽  
Spiking Neural P Systems

The computational power of timed P systems with active membranes using promoters

2018 ◽  
Vol 29 (5) ◽  
pp. 663-680 ◽  
Author(s):  
YUEGUO LUO ◽  
HAIJUN TAN ◽  
YING ZHANG ◽  
YUN JIANG
Keyword(s):  
P Systems ◽  
P System ◽  
Computational Power ◽  
Computable Set ◽  
Complete Problem ◽  
Active Membranes ◽  
New Variant ◽  
Uniform Solution ◽  
Bioinspired Computing ◽  
Computing Models

P systems with active membranes are a class of bioinspired computing models, where the rules are used in the non-deterministic maximally parallel manner. In this paper, first, a new variant of timed P systems with active membranes is proposed, where the application of rules can be regulated by promoters with only two polarizations. Next, we prove that any Turing computable set of numbers can be generated by such a P system in the time-free way. Moreover, we construct a uniform solution to the$\mathcal{SAT}$problem in the framework of such recognizer timed P systems in polynomial time, and the feasibility and effectiveness of the proposed system is demonstrated by an instance. Compared with the existing methods, the P systems constructed in our work require fewer necessary resources and RS-steps, which show that the solution is effective toNP-complete problem.

Membrane Computing

2017 ◽  
pp. 15-34
Keyword(s):  
Robot Control ◽  
Membrane Computing ◽  
P Systems ◽  
System Model ◽  
P System ◽  
Input Output ◽  
Control Models ◽  
P Colonies ◽  
Theoretical Computing ◽  
Computing Models

The theoretical computing models that are used throughout this book are described in this chapter. These models are based on the initial P system model and include: Numerical P systems, Enzymatic Numerical P systems, P colonies and P swarms. Detailed examples and execution diagrams help the reader allow the reader to understand the functioning principle of each model and also its potential in various applications. The similarity between P systems (and their variants) and robot control models is also addressed. This analysis is presented to the reader in a side-by-side manner using a table where each row represents an analysis topic. Among others we mention: (1) Architectural structure, (2) Modularity and hierarchy, (3) Input-output relationships, (4) Parallelism.

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