小通用同质脉冲神经膜系统

最近,许多学者针对寻找基于脉冲神经膜系统的小通用计算设备问题进行了研究.脉冲神经膜系统是一种源于神经元之间通过电子脉冲传递信息方式的分布式、并行计算模型.同质脉冲神经膜系统是指一种系统中所有神经元具有相同规则集合的脉冲神经膜系统的受限变体.本文研究了同质脉冲神经膜系统的小通用性:在使用标准规则和权值情况下,作为计算函数的装置,需要53个神经元可以构造一个通用同质脉冲神经膜系统;作为产生数的装置,则需要52个神经元.     

脉冲神经膜系统求解任意两个自然数的乘积

考虑在一种新的生物计算装置(即脉冲神经膜系统)上处理任意两个自然数的乘积问题.首先给出了具有单个输入神经元的脉冲神经膜系统,它可以求解n-addition问题(即n个自然数的求和);其次,构造了一族脉冲神经膜系统,使该族中的每个系统可以求解给定二进制位长度的任意两个自然数的乘积.文中解决了Miguel AGutierrez-Naranjo和Alberto Leporati提出的一个公开问题.     

带反脉冲的同质脉冲神经膜系统

脉冲神经膜系统是一种膜系统中吸收了脉冲神经网络特点的新型生物计算装置,具有强大的计算能力.带反脉冲的同质脉冲神经膜系统是使用了两种对象(称为脉冲和反脉冲)、且其中每个神经元具有相同规则集合的一种脉冲神经膜系统的变体.本文研究了无延迟规则和突触权值情况下的带反脉冲同质脉冲神经膜系统的计算通用性问题,证明了这种P系统无论是工作在产生模式,还是接收模式下都是计算通用的.本文解答了曾湘祥等人提出的关于是否存在无延迟规则的同质脉冲神经膜系统和如何移除突触权值的两个公开问题.     

通用的不带延迟的同质脉冲神经膜系统

脉冲神经膜系统是一种膜系统中吸收了脉冲神经网络特点的新型生物计算装置,具有强大的计算能力。同质脉冲神经膜系统是指一种所有神经元具有相同规则集合的脉冲神经膜系统的变体。研究了突触上带权值和突触上不带权值的两种同质脉冲神经膜系统在不使用具有延迟的激发规则情况下的计算通用性问题,并证明了这两种不带延迟的同质脉冲神经膜系统无论是工作在产生模式下,还是工作在接收模式下都是计算通用的。解决了曾湘祥、张兴义和潘林强提出的关于不带延迟的同质脉冲神经膜系统是否具有计算通用性的公开问题。     

自然计算的新分支——膜计算

作为自然计算的新分支,膜计算是当前计算机科学、数学、生物学和人工智能等多学科交叉的研究热点.概述膜计算的最新动态,以一个简单膜系统为例介绍膜计算的基本概念和基本原理,从细胞型、组织型和神经型三类膜系统以及它们的计算能力和计算效率方面介绍膜计算理论研究进展,通过概括膜计算国内外应用研究成果讨论其应用前景和方向,并从软硬件发展历程分析膜系统软硬实现研究现状.最后给出有关膜计算研究的重要网络资源、热点研究领域和重点关注的问题.     

脉冲神经膜系统形式化验证仿真与分析

脉冲神经膜系统的形式化验证通常很复杂,目前还没有一种通用的方法.本文基于SnpsGUI仿真软件,例证了两个脉冲神经膜系统的形式化验证过程,重点分析并揭示了格局转移图和脉冲神经膜系统之间的内在联系,并总结出了3个一般性结论,达到了通过计算机辅助验证脉冲神经膜系统正确性与完整性的目的.结论显示,格局转移图是一种解决脉冲神经膜系统形式化验证的有效方法,SnpsGUI是脉冲神经膜系统形式化验证的有力辅助工具.同时,对基于脉冲神经膜系统更有效的形式化验证方法提出了展望,对SnpsGUI仿真软件进行了评述,提出了改进方向.     

脉冲神经膜系统在穷举使用规则下产生的二进制字符串语言

脉冲神经膜系统是基于大脑中神经元之间通过突触相瓦协作、处理脉冲的生物现象提出的一种新的模型,文中在穷举使用规则的情况下考虑将脉冲神经膜系统作为串语言产生器:当输出神经元发送出一个或多个神经脉冲时,用数字1表示,否则用数字0表示,当计算停止时,把产生的二进制串定义为系统的计算结果.在文中,作者让明了在穷举使用规则的情况下,具有一个神经元的脉冲神经膜系统可以刻画二进制有限语言,并且证明了在不限制神经元个数的情况下,该系统可以刻画递归可枚举语言.     

全局异步局部同步的带阈值的脉冲神经膜系统

带阈值的脉冲神经膜系统是一类生物启发式计算模型，提出该系统的灵感来自神经元电位变化与其活动的联系。对于带阈值的脉冲神经膜系统的计算能力研究，人们已证明该系统在极大同步工作模式下，作为产生数或接受数的计算设备时，是与图灵机等价(计算通用)的，而该系统在其他工作模式下的计算能力如何也是人们普遍关心的问题。文中研究的是带阈值脉冲神经膜系统在全局异步局部同步模式下产生数的能力，证明了突触带整数权重的相应系统是计算通用的，而突触带正整数权重的相应系统只能产生半线性数集。研究结果表明，突触权重的取值范围影响着全局异步局部同步工作模式下带阈值脉冲神经膜系统的计算能力。     

带反脉冲的脉冲神经膜系统实现对称三值逻辑与算术运算

脉冲神经膜系统(简称SN P系统)是一种起源于神经元通过电子脉冲传递信息方式的新型分布式、并行计算模型,具有强大的计算能力和解决计算难问题的潜力.带反脉冲的脉冲神经膜系统(简称SN PA系统)是一种包含脉冲、反脉冲两种对象的脉冲神经膜系统的变体,非常适合于对称三值数字的编码.本文使用带反脉冲的脉冲神经膜系统模拟了对称三值的通用与、或、非逻辑门的功能,也实现了对称三值的整数加、减算术运算的功能.目前的工作是基于带反脉冲的脉冲神经膜系统的三值型CPU设计在理论上的首次尝试.本文也为潘林强和Paun G提出的一个公开问题提供了一种实用案例.     

脉冲神经膜系统实现有符号整数的算术运算

脉冲神经膜系统是一种结合脉冲神经网络和膜系统特点的新型生物计算装置,具有强大的计算能力和解决计算难问题的潜力.本文考虑在脉冲神经膜系统这种装置上处理一些简单的算术运算问题,包括二进制补码转换、有符号整数的加、减运算和任意两个自然数的乘法运算,这些系统的输入、输出数均采用二进制方式,编码采用合适的脉冲序列.本文较好地解决了Gutiérrez-Naranjo MA.和Leporati A.提出的关于如何实现两个任意自然数乘法运算的公开问题.当前工作可以作为解决更加复杂问题的基础,也有助于设计基于脉冲神经膜系统的生物型CPU.     

A quick overview of membrane computing with some details about spiking neural P systems

We briefly present the basic elements of membrane computing, a branch of natural computing inspired by the structure and functioning of living cells, then we give some details about spiking neural P systems, a class of membrane systems recently introduced, with motivations related to the way neurons communicate by means of spikes. In both cases, of general P systems and of spiking neural P systems, we introduce the fundamental concepts, give a few examples, then recall the types of results and of applications. A series of bibliographical references are provided.     

Spiking neural P systems with neuron division and budding

Spiking neural P systems are a class of distributed and parallel computing models inspired by spiking neurons.In this work,the features of neuron division and neuron budding are introduced into the framework of spiking neural P systems,which are processes inspired by neural stem cell division. With neuron division and neuron budding,a spiking neural P system can generate exponential work space in polynomial time as the case for P systems with active membranes.In this way,spiking neural P systems can efficie...     

Small universal simple spiking neural P systems with weights

Spiking neural P systems with weights（WSN P systems,for short） are a new variant of spiking neural P systems,where the rules of a neuron are enabled when the potential of that neuron equals a given value.It is known that WSN P systems are universal by simulating register machines. However,in these universal systems,no bound is considered on the number of neurons and rules. In this work,a restricted variant of WSN P systems is considered,called simple WSN P systems,where each neuron has only one rule. The complexity parameter,the number of neurons,to construct a universal simple WSN P system is investigated. It is proved that there is a universal simple WSN P system with 48 neurons for computing functions; as generator of sets of numbers,there is an almost simple（that is,each neuron has only one rule except that one neuron has two rules） and universal WSN P system with 45 neurons.     

On the power of elementary features in spiking neural P systems

Since their first publication in 2006, spiking neural (SN) P systems have already attracted the attention of a lot of researchers. This might be owing to the fact that this abstract computing device follows basic principles known from spiking neural nets, but its implementation is discrete, using membrane computing background. Among the elementary properties which confer SN P systems their computational power one can count the unbounded fan-in (indegree) and fan-out (outdegree) of each “neuron”, synchronicity of the whole system, the possibility of delaying and/or removing spikes in neurons, the capability of evaluating arbitrary regular expressions in neurons in constant time and some others. In this paper we focus on the power of these elementary features. Particularly, we study the power of the model when some of these features are disabled. Rather surprisingly, even very restricted SN P systems keep their universal computational power. Certain important questions regarding this topic still remain open.     

Reversible spiking neural P systems

Spiking neural (SN) P systems are a class of distributed parallel computing devices inspired by the way neurons communicate by means of spikes. In this work, we investigate reversibility in SN P systems, as well as the computing power of reversible SN P systems. Reversible SN P systems are proved to have Turing creativity, that is, they can compute any recursively enumerable set of non-negative integers by simulating universal reversible register machine.     

On languages generated by asynchronous spiking neural P systems

In this paper, we investigate the languages generated by asynchronous spiking neural P systems. Characterizations of finite languages and recursively enumerable languages are obtained by asynchronous spiking neural P systems with extended rules. The relationships of the languages generated by asynchronous spiking neural P systems with regular and non-semilinear languages are also investigated.     

Spiking neural P systems: An improved normal form

Spiking neural P systems (in short, SN P systems) are computing devices based on the way the neurons communicate through electrical impulses (spikes). These systems involve various ingredients; among them, we mention forgetting rules and the delay in firing rules. However, it is known that the universality can be obtained without using these two features. In this paper we improve this result in two respects: (i) each neuron contains at most two rules (which is optimal for systems used in the generative mode), and (ii) the rules in the neurons using two rules have the same regular expression which controls their firing. This result answers a problem left open in the literature, and, in this context, an incompleteness in some previous proofs related to the elimination of forgetting rules is removed. Moreover, this result shows a somewhat surprising uniformity of the neurons in the SN P systems able to simulate Turing machines, which is both of a theoretical interest and it seems to correspond to a biological reality. When a bound is imposed on the number of spikes present in a neuron at any step of a computation (such SN P systems are called finite), two surprising results are obtained. First, a characterization of finite sets of numbers is obtained in the generative case (this contrasts the case of other classes of SN P systems, where characterizations of semilinear sets of numbers are obtained for finite SN P systems). Second, the accepting case is strictly more powerful than the generative one: all finite sets and also certain arithmetical progressions can be accepted. A precise characterization of the power of accepting finite SN P systems without forgetting rules and delay remains to be found.