Discovering Boolean Gates in Slime Mould

TL;DR

This paper demonstrates that Physarum polycephalum slime mould can be configured to function as any 2-input Boolean logic gate by exploiting its electrical properties, using both experimental and neural network-based methods.

Contribution

It introduces a novel approach to implement Boolean logic gates in slime mould through electrical configuration and neural network optimization, expanding unconventional computing methods.

Findings

Physarum can act as any 2-input Boolean gate.

Configurations are found via experimental sweeping and neural network training.

Gates' occurrence frequency analyzed and compared to other materials.

Abstract

Slime mould of Physarum polycephalum is a large cell exhibiting rich spatial non-linear electrical characteristics. We exploit the electrical properties of the slime mould to implement logic gates using a flexible hardware platform designed for investigating the electrical properties of a substrate (MECOBO). We apply arbitrary electrical signals to `configure' the slime mould, i.e. change shape of its body and, measure the slime mould's electrical response. We show that it is possible to find configurations that allow the Physarum to act as any 2-input Boolean gate. The occurrence frequency of the gates discovered in the slime was analysed and compared to complexity hierarchies of logical gates obtained in other unconventional materials. The search for gates was performed by both sweeping across configurations in the real material as well as training a neural network-based model and searching the gates therein using gradient descent.