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Weighted spiking neural P systems with polarizations and anti-spikes

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Abstract

Taking neuron-associated polarizations as the firing condition of neuron rules, the concept of polarization is applied to spiking neural P systems (SN P systems), which is adopted to effectively simulate the biological fact that every neuron in the nervous system contains electrical charges. In addition, in biological neural systems, neurons are always connected by multiple synapses. Therefore, a novel computational model referred to as the weighted spiking neural P systems with polarizations and anti-spikes (PAWSN P systems) is obtained by introducing weighted synapses and anti-spikes. The application of anti-spikes further optimizes the computational performance of the systems. We investigate the computational universality of PAWSN P systems. By simulating the register machine, it is verified that PAWSN P systems are able to accomplish the generation and acceptance of numbers, which provides justification for stating that the PAWSN P systems have Turing universality. With further exploration, we apply three types of polarizations and 98 neurons to construct a small universal PAWSN P system, which is capable of computing functions. By comparing several kinds of extended SN P systems, the results show that the PAWSN P systems achieve universality using fewer computational resources.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No.61806114, 61472231, 61876101, 61602282, 61402187, 61502283, 61802234, 61703251), the China Postdoctoral Science Foundation (No.2018M642695, 2019T120607).

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    Yuping Liu & Yuzhen Zhao

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Liu, Y., Zhao, Y. Weighted spiking neural P systems with polarizations and anti-spikes. J Membr Comput 4, 269–283 (2022). https://doi.org/10.1007/s41965-022-00112-7

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