Electrical activity of fungi: Spikes detection and complexity analysis

TL;DR

This paper introduces novel methods for detecting and classifying electrical spikes in fungi, analyzing their complexity to understand fungal communication and growth processes, which differ from neural activity.

Contribution

It presents original techniques tailored for fungal spike detection and classification, and applies information-theoretic analysis to fungal electrical activity.

Findings

Fungal electrical activity exhibits high variability unlike neural signals.

Proposed methods effectively detect and classify fungal spikes.

Complexity analysis reveals insights into fungal communication mechanisms.

Abstract

Oyster fungi \emph{Pleurotus djamor} generate actin potential like spikes of electrical potential. The trains of spikes might manifest propagation of growing mycelium in a substrate, transportation of nutrients and metabolites and communication processes in the mycelium network. The spiking activity of the mycelium networks is highly variable compared to neural activity and therefore can not be analysed by standard tools from neuroscience. We propose original techniques for detecting and classifying the spiking activity of fungi. Using these techniques, we analyse the information-theoretic complexity of the fungal electrical activity. The results can pave ways for future research on sensorial fusion and decision making of fungi.