Business School, Shandong Normal University, Jinan, China.
Comput Intell Neurosci. 2021 Aug 25;2021:3285719. doi: 10.1155/2021/3285719. eCollection 2021.
The nonlinear spiking neural P systems (NSNP systems) are new types of computation models, in which the state of neurons is represented by real numbers, and nonlinear spiking rules handle the neuron's firing. In this work, in order to improve computing performance, the weights and delays are introduced to the NSNP system, and universal nonlinear spiking neural P systems with delays and weights on synapses (NSNP-DW) are proposed. Weights are treated as multiplicative constants by which the number of spikes is increased when transiting across synapses, and delays take into account the speed at which the synapses between neurons transmit information. As a distributed parallel computing model, the Turing universality of the NSNP-DW system as number generating and accepting devices is proven. 47 and 43 neurons are sufficient for constructing two small universal NSNP-DW systems. The NSNP-DW system solving the Subset Sum problem is also presented in this work.
非线性尖峰神经网络 P 系统（NSNP 系统）是新型的计算模型，其中神经元的状态由实数表示，而非线性尖峰规则处理神经元的激发。在这项工作中，为了提高计算性能，向 NSNP 系统中引入了权重和延迟，提出了具有突触权重和延迟的通用非线性尖峰神经网络 P 系统（NSNP-DW）。权重被视为乘法常数，当神经元之间的突触跨越时，通过权重可以增加尖峰的数量，而延迟考虑了神经元之间传递信息的速度。作为分布式并行计算模型，证明了 NSNP-DW 系统作为生成和接受设备的图灵通用性。构建两个小的通用 NSNP-DW 系统需要 47 和 43 个神经元。本文还提出了用于解决子集和问题的 NSNP-DW 系统。
