Spiking neural P systems (SNP systems) are a class of distributed and parallel computation models, which are inspired by the way in which neurons process information through spikes, where the integrate-and-fire behavior of neurons and the distribution of produced spikes are achieved by spiking rules. In this work, a novel mechanism for separately describing the integrate-and-fire behavior of neurons and the distribution of produced spikes, and a novel variant of the SNP systems, named evolution-communication SNP (ECSNP) systems, is proposed. More precisely, the integrate-and-fire behavior of neurons is achieved by spike-evolution rules, and the distribution of produced spikes is achieved by spike-communication rules. Then, the computational power of ECSNP systems is examined. It is demonstrated that ECSNP systems are Turing universal as number-generating devices. Furthermore, the computational power of ECSNP systems with a restricted form, i.e. the quantity of spikes in each neuron throughout a computation does not exceed some constant, is also investigated, and it is shown that such restricted ECSNP systems can only characterize the family of semilinear number sets. These results manifest that the capacity of neurons for information storage (i.e. the quantity of spikes) has a critical impact on the ECSNP systems to achieve a desired computational power.

中文翻译：

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进化通信脉冲神经 P 系统

脉冲神经 P 系统（SNP 系统）是一类分布式并行计算模型，其灵感来自神经元通过脉冲处理信息的方式，实现神经元的集成和激发行为以及产生的脉冲的分布通过扣球规则。在这项工作中，提出了一种新的机制，用于分别描述神经元的整合和激发行为和产生的尖峰的分布，以及 SNP 系统的一种新变体，称为进化通信 SNP (ECSNP) 系统。更准确地说，神经元的集成和激发行为是通过尖峰进化规则实现的，产生的尖峰分布是通过尖峰通信规则实现的。然后，检查 ECSNP 系统的计算能力。证明 ECSNP 系统作为数字生成设备是图灵通用的。此外，还研究了具有受限形式的 ECSNP 系统的计算能力，即在整个计算过程中每个神经元中的尖峰数量不超过某个常数，并且表明这种受限的 ECSNP 系统只能表征半线性族数集。这些结果表明，神经元用于信息存储的能力（即尖峰的数量）对 ECSNP 系统实现所需的计算能力具有至关重要的影响。结果表明，这种受限的 ECSNP 系统只能表征半线性数集族。这些结果表明，神经元用于信息存储的能力（即尖峰的数量）对 ECSNP 系统实现所需的计算能力具有至关重要的影响。结果表明，这种受限的 ECSNP 系统只能表征半线性数集族。这些结果表明，神经元用于信息存储的能力（即尖峰的数量）对 ECSNP 系统实现所需的计算能力具有至关重要的影响。