Spiking neural P systems: main ideas and results

Abstract

Spiking neural P systems are parallel and distributed computation devices which are inspired by the neuro-physiological behavior of biological neurons. In this paper we will present, with a tutorial approach, the main underlying ideas and the most interesting variants that have been proposed in the literature. In particular, we will discuss the results on the computational power of these models, both in terms of Turing completeness and of efficiency in solving hard problems, under different assumptions for information encoding, form and application of rules, and bounds on the main parameters defining the systems.

Solving the SAT problem using spiking neural P systems with coloured spikes and division rules

“…One such type of cell inspiring many computational models is the neuron. The structure and function of biological neurons communicating via sending impulses (spikes) was the main motivation behind the construction of a popular variant of membrane computing model known as the spiking neural P system (SNPS) [2,3]. Since SNNs (i.e., spiking neural networks) [4] belong to third-generation neural networks, SNPS models can also be considered third-generation neural networks.…”