Pan, Linqiang

Rodríguez-Patón, Alfonso

Song, Tao

Wong, M. L. Dennis

Wu, Tingfang

Zheng, Pan

English

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.</p

Heriot Watt Pure

Spiking Neural P Systems with Learning Functions

https://pureapps2.hw.ac.uk/ws/files/24012786/SNP_Rec.pdf

Spiking Neural P Systems with Learning Functions

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Heriot Watt Pure

Heriot Watt Pure