Threshold sensing yields optimal path formation in Physarum polycephalum

TL;DR

This paper demonstrates that threshold sensing in Physarum polycephalum leads to the formation of optimal and flexible paths for maze solving, revealing underlying mechanisms of its decentralized problem-solving abilities.

Contribution

It introduces a circuital network model showing how threshold sensing results in optimal path formation and adaptability in Physarum polycephalum, advancing understanding of primitive intelligence.

Findings

Threshold sensing produces unique, optimal paths.

Conditions for alternative paths are identified.

Results align with experimental observations.

Abstract

The model organism Physarum polycephalum is known to perform decentralised problem solving despite absence of nervous system. Experimental evidence and modelling studies have linked these abilities, and in particular maze-solving, to some sort of memory and adaptation. However, despite compelling hypotheses, it is still not clear whether the tasks are solved optimally, and which key dynamical mechanisms enable Physarum's impressive abilities. Here, we employ a circuital network model for the foraging behaviour of Physarum polycephalum to prove that threshold sensing yields the emergence of unique and optimal paths that connect food sources and solve mazes. We also prove which conditions lead to alternative paths, thus elucidating how the organism achieves flexibility and adaptation in a self-organised manner. These findings are aligned with experimental evidences and provide insight into the evolution of primitive intelligence. Our results can also inspire the development of threshold-based algorithms for computing applications.