Abstract
Spiking neural P systems (SN P systems), inspired by biological neurons, are introduced as symbolical neural-like computing models that encode information with multisets of symbolized spikes in neurons and process information by using spike-based rewriting rules. Inspired by neuronal activities affected by enzymes, numerical variants of SN P systems called enzymatic numerical spiking neural P systems (ENSNP systems) are proposed wherein each neuron has a set of variables with real values and a set of enzymatic activation-production spiking rules, and each synapse has an assigned weight. By using spiking rules, ENSNP systems can directly implement mathematical methods based on real numbers and continuous functions. Furthermore, ENSNP systems are used to model ENSNP membrane controllers for robots implementing wall following. The trajectories, distances from the wall, and wheel speeds of robots with ENSNP membrane controllers for wall following are compared with those of a robot with a membrane controller for wall following. The average error values of the designed ENSNP membrane controllers are compared with three recently fuzzy logical controllers with optimization algorithms for wall following. The experimental results showed that the designed ENSNP membrane controllers can be candidates as efficient controllers to control robots implementing the task of wall following.