Spiking neural P systems incorporating winner-take-all mechanism

Spiking neural P systems (SNP systems), a class of parallel distributed computational models inspired by biological neurons, have become an important research direction in biocomputing in recent years due to their biological interpretability and low-power computing. There are many studies on the expressive power of SNP system variants, but their computational efficiency is not high in terms of resource overhead. Inspired by the biological winner-take-all (WTA) computation mechanism, this study proposes spiking neural P systems incorporating WTA mechanism (WTASNP systems). Competing neuron nodes are introduced into the WTASNP systems to realize the competitive selection and inhibition control mechanisms of the WTA computation. After competition, only the winner neuron is allowed to emit spikes, while the loser neurons are inhibited. It is proved that the WTASNP systems have Turing universality. Furthermore, it reduces the computational resource requirements for solving the NP-complete SAT problem with SNP systems from O(n2) or O(2n) complexity levels down to linearly solvable O(n). The WTASNP systems not only effectively preserve the preamble spike information, but also inhibits the loser neuron spike issuance by the WTA computational mechanism of competing neurons, reduces redundant computation to avoid neuron over-excitation, and improves the computational efficiency and expressive power.