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.

中文翻译：

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用于逻辑门设计的非常规仿生模型

在过去的几年中，一种经过充分研究的生物基质，即多头泡泡菌，已被证明可以有效地在难以解决的复杂数学问题中找到合适且有效的解决方案。P. polycephalum 疟原虫是一个单细胞，是一个繁荣的生物计算例子。因此，它在过去已成功用于解决图形中的各种路径问题和组合问题。在这项工作中，这个有趣的行为被一个强大的非常规计算模型模仿，从细胞和学习自动机的概念中汲取灵感。也就是说，我们采用具有学习能力的元胞自动机 (CA) 原理来开发强大的计算模型，能够对上述生物基质进行适当的建模，因此，捕捉其计算能力。CA 在模拟生物系统和解决科学问题方面非常有效，因为它们能够体现系统的基本特性，在该系统中，全局行为是作为简单组件在局部相互作用的结果而产生的。在将 CA 与学习能力相结合后，由此产生的计算工具应该适用于对生物体的行为进行建模。因此，所提出的仿生模型的固有能力和计算特性被强调对 Physarum 模拟逻辑门的能力的实验验证，同时试图在具有食物来源的正确配置的迷宫中找到最小路径。各种逻辑门的模拟结果在定性和定量上都非常吻合，