Spiking neural P systems with long-term potentiation and depression

Abstract

Spiking neural P systems (SNP systems) are parallel, distributed, low energy consuming, non-deterministic, interpretable and simple structured computational models abstracted from biological neural systems. In this work, to make SNP systems have the ability to adjust synaptic weights according to changes in the environment and boost SNP systems' learning ability and intelligence, long-term potentiation/depression mechanisms in the signal transmission of two neurons are abstracted from biological neural systems and introduced into SNP systems. As result, a type of novel SNP system, called SNP systems with long-term potentiation and depression (LTPD-SNP systems), is developed. Based on novel synaptic weight learning rules in LTPD-SNP systems, the weights of synapses change dynamically depending on the intensity and timing of the spikes passing through them and their own properties. The universality of LTPD-SNP systems as number generation and acceptance devices is proved. A uniform solution to SAT problem is designed to study the computational efficiency of the LTPD-SNP systems. Simultaneously, an anomaly detection method for AC engines based on LTPD-SNP systems is constructed to show how the system can solve a learning task. This work improves the learning ability and intelligence of SNP systems, and offers some ideas for creating brain-like intelligent computational models.