  During the last years, a well studied biological substrate, namely Physarum
polycephalum, has been proven efficient on finding appropriate and efficient
solutions in hard to solve complex mathematical problems. The plasmodium of P.
polycephalum is a single-cell that serves as a prosperous bio-computational
example. Consequently, it has been successfully utilized in the past to solve a
variety of path problems in graphs and combinatorial problems. In this work,
this interesting behaviour is mimicked by a robust unconventional computational
model, drawing inspiration from the notion of Cellular and Learning Automata.
Namely, we employ principles of Cellular Automata (CAs) enriched with learning
capabilities to develop a robust computational model, able of modelling
appropriately the aforementioned biological substrate and, thus, capturing its
computational capabilities. CAs are very efficient in modelling biological
systems and solving scientific problems, owing to their ability of incarnating
essential properties of a system where global behaviour arises as an effect of
simple components, interacting locally. The resulting computational tool, after
combining CAs with learning capabilities, should be appropriate for modelling
the behaviour of living organisms. Thus, the inherent abilities and
computational characteristics of the proposed bio-inspired model are stressed
towards the experimental verification of Physarum's ability to model Logic
Gates, while trying to find minimal paths in properly configured mazes with
food sources. The presented simulation results for various Logic Gates are
found in good agreement, both qualitatively and quantitatively, with the
corresponding experimental results, proving the efficacy of this unconventional
bio-inspired model and providing useful insights for its enhanced usage in
various computing applications.
