A Parallel Workflow Pattern Modeling Using Spiking Neural P Systems With Colored Spikes

Spiking neural P systems, otherwise known as named SN P systems, are bio-inspired parallel and distributed neural-like computing models. Due to the spiking behavior, SN P systems fall into the category of spiking neural networks, and are considered to be an auspicious candidate of the 3G of neural networks. It has been reported that SN P systems with colored spikes are computationally capable, and perform well in describing behaviors of complex systems. Nonetheless, some practical issue is open to be investigate, such as workflow and traffic flow modeling. In this paper, a parallel workflow pattern modeling using SN P systems with colored spikes is proposed. As results, 20 designs are constructed using SN P systems for 20 classical workflow patterns. The functioning processes that operate both sequentially and simultaneously in the workflow pattern are able to be modeled and simulated. SN P systems with colored spikes have some similarity with Petri nets, hence can be used to model workflow patterns. This will provide a novel neural-like modeling method for modeling traffic flow.