IEEE Trans Nanobioscience. 2019 Apr;18(2):176-190. doi: 10.1109/TNB.2019.2896981. Epub 2019 Feb 1.
Spiking neural P systems (SN P systems) are a class of distributed and parallel neural-like computing models, inspired from the way neurons communicate by means of spikes. In this paper, a new variant of the systems, called SN P systems with learning functions, is introduced. Such systems can dynamically strengthen and weaken connections among neurons during the computation. A class of specific SN P systems with simple Hebbian learning function is constructed to recognize English letters. The experimental results show that the SN P systems achieve average accuracy rate 98.76% in the test case without noise. In the test cases with low, medium, and high noises, the SN P systems outperform back propagation neural networks and probabilistic neural networks. Moreover, comparing with spiking neural networks, SN P systems perform a little better in recognizing letters with noise. The result of this paper is promising in terms of the fact that it is the first attempt to use SN P systems in pattern recognition after many theoretical advancements of SN P systems, and SN P systems exhibit the feasibility for tackling pattern recognition problems.
尖峰神经网络系统 (Spiking Neural P Systems, SN P systems) 是一类分布式、并行的类神经计算模型，灵感来源于神经元通过尖峰进行通信的方式。在本文中，引入了一种新型的系统，称为具有学习功能的尖峰神经网络系统。在计算过程中，这些系统可以动态地增强或减弱神经元之间的连接。构建了一类具有简单赫布学习功能的特定尖峰神经网络系统，用于识别英文字母。实验结果表明，在无噪声的测试案例中，SN P 系统的平均准确率达到 98.76%。在低、中、高噪声测试案例中，SN P 系统的表现优于反向传播神经网络和概率神经网络。此外，与尖峰神经网络相比，SN P 系统在识别带噪声的字母方面表现稍好。在尖峰神经网络系统在经历了许多理论上的发展之后，首次尝试将其应用于模式识别，并且 SN P 系统表现出了处理模式识别问题的可行性，这一结果具有很大的发展潜力。
