1. Spiking Neural P Systems With Learning Functions
- Author
-
Pan Zheng, Linqiang Pan, M. L. Dennis Wong, Tao Song, Tingfang Wu, and Alfonso Rodríguez-Patón
- Subjects
Spiking neural network ,Artificial neural network ,Computer science ,business.industry ,Biomedical Engineering ,Probabilistic logic ,Pharmaceutical Science ,Medicine (miscellaneous) ,Bioengineering ,02 engineering and technology ,021001 nanoscience & nanotechnology ,Computer Science Applications ,Machine Learning ,Noise ,Hebbian theory ,Pattern recognition (psychology) ,Neural Networks, Computer ,Artificial intelligence ,Electrical and Electronic Engineering ,Bio-inspired computing ,0210 nano-technology ,business ,Membrane computing ,Biotechnology - Abstract
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.
- Published
- 2019