IEEE Trans Nanobioscience. 2018 Oct;17(4):560-566. doi: 10.1109/TNB.2018.2879345. Epub 2018 Nov 2.
Spiking neural P systems (SNP systems) are parallel and non-deterministic models of computation, inspired by the neural system of the brain. A variant of SNP systems known as SNP systems with structural plasticity (SNPSP systems) includes the feature of adding or removing synapses among neurons. This feature is inspired by plasticity from neuroscience during cognition and learning. Despite the reductionist framework of SNP and SNPSP systems, such as brain-like systems are capable of computational universality. In particular, we use SNPSP systems in this paper to compute some classes of languages from the Chomsky hierarchy: FIN, REG, and RE. The computations of such classes continue a research direction established in the previous paper. We also emphasize the (dis)advantages of synapse plasticity in the neural system, compared with existing features of SNP systems, when generating languages.
尖峰神经网络系统（SNP 系统）是一种受大脑神经系统启发的并行、非确定性计算模型。SNP 系统的一种变体，即具有结构可塑性的 SNP 系统（SNPSP 系统），包括在神经元之间添加或删除突触的功能。这种功能受到认知和学习过程中神经科学可塑性的启发。尽管 SNP 和 SNPSP 系统具有还原论框架，但类似大脑的系统能够实现计算的普遍性。特别是，在本文中，我们使用 SNPSP 系统来计算乔姆斯基层次结构中的一些语言类：FIN、REG 和 RE。这些语言类的计算延续了前一篇论文中确立的研究方向。我们还强调了在生成语言时，与 SNP 系统现有的特征相比，神经网络系统中突触可塑性的（不）优势。
