School of Electrical Engineering and Electronic Information and Key Laboratory of Fluid and Power Machinery, Ministry of Education, Xihua University, Chengdu, Sichuan 610039, P. R. China.
Research Group on Natural Computing, Department of Computer Science and Artificial Intelligence, University of Sevilla, Sevilla, Andalucía 41004, Spain.
Int J Neural Syst. 2021 Jan;31(1):2050042. doi: 10.1142/S0129065720500422. Epub 2020 Jul 23.
Based on the feature and communication of neurons in animal neural systems, spiking neural P systems (SN P systems) were proposed as a kind of powerful computing model. Considering the length of axons and the information transmission speed on synapses, SN P systems with delay on synapses (SNP-DS systems) are proposed in this work. Unlike the traditional SN P systems, where all the postsynaptic neurons receive spikes at the same instant from their presynaptic neuron, the postsynaptic neurons in SNP-DS systems would receive spikes at different instants, depending on the delay time on the synapses connecting them. It is proved that the SNP-DS systems are universal as number generators. Two small universal SNP-DS systems, with standard or extended rules, are constructed to compute functions, using 56 and 36 neurons, respectively. Moreover, a simulator has been provided, in order to check the correctness of these two SNP-DS systems, thus providing an experimental validation of the universality of the systems designed.
基于动物神经系统中神经元的特征和通信，提出了尖峰神经网络 P 系统（SNP 系统）作为一种强大的计算模型。考虑到轴突的长度和突触上的信息传输速度，本文提出了具有突触延迟的 SNP 系统（SNP-DS 系统）。与传统的 SNP 系统不同，传统 SNP 系统中所有的突触后神经元都在同一时刻从其突触前神经元接收尖峰，而 SNP-DS 系统中的突触后神经元将在不同的时刻接收尖峰，这取决于连接它们的突触上的延迟时间。证明 SNP-DS 系统是通用的数字生成器。构建了两个具有标准或扩展规则的小型通用 SNP-DS 系统，分别使用 56 个和 36 个神经元来计算函数。此外，还提供了一个模拟器，以检查这两个 SNP-DS 系统的正确性，从而为设计的系统的通用性提供了实验验证。
