Zhang Xingyi, Luo Bin, Fang Xianyong, Pan Linqiang
Key Lab of Intelligent Computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei, China.
Biosystems. 2012 Apr-Jun;108(1-3):52-62. doi: 10.1016/j.biosystems.2012.01.007. Epub 2012 Jan 24.
Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes, where neurons work in parallel in the sense that each neuron that can fire should fire, but the work in each neuron is sequential in the sense that at most one rule can be applied at each computation step. In this work, we consider SN P systems with the restriction that at most one neuron can fire at each step, and each neuron works in an exhaustive manner (a kind of local parallelism - an applicable rule in a neuron is used as many times as possible). Such SN P systems are called sequential SN P systems with exhaustive use of rules. The computation power of sequential SN P systems with exhaustive use of rules is investigated. Specifically, characterizations of Turing computability and of semilinear sets of numbers are obtained, as well as a strict superclass of semilinear sets is generated. The results show that the computation power of sequential SN P systems with exhaustive use of rules is closely related with the types of spiking rules in neurons.
脉冲神经P系统（简称为SNP系统）是一类分布式并行计算设备，其灵感来源于神经元通过脉冲进行通信的方式。在这种系统中，神经元并行工作，即每个能够激发的神经元都应该激发，但每个神经元内部的工作是顺序的，也就是说在每个计算步骤中最多只能应用一条规则。在这项工作中，我们考虑具有以下限制的SNP系统：在每个步骤中最多只有一个神经元可以激发，并且每个神经元以穷举方式工作（一种局部并行性——神经元中的适用规则会被尽可能多地使用）。这样的SNP系统被称为规则穷举使用的顺序SNP系统。我们研究了规则穷举使用的顺序SNP系统的计算能力。具体而言，得到了图灵可计算性和数字半线性集的特征，并且生成了半线性集的一个严格超类。结果表明，规则穷举使用的顺序SNP系统的计算能力与神经元中脉冲规则的类型密切相关。
