Language of fungi derived from their electrical spiking activity

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<jats:p> Fungi exhibit oscillations of extracellular electrical potential recorded via differential electrodes inserted into a substrate colonized by mycelium or directly into sporocarps. We analysed electrical activity of ghost fungi ( <jats:italic>Omphalotus nidiformis</jats:italic> ), Enoki fungi ( <jats:italic>Flammulina velutipes</jats:italic> ), split gill fungi ( <jats:italic>Schizophyllum commune</jats:italic> ) and caterpillar fungi ( <jats:italic>Cordyceps militaris</jats:italic> ). The spiking characteristics are species specific: a spike duration varies from 1 to 21 h and an amplitude from 0.03 to 2.1 mV. We found that spikes are often clustered into trains. Assuming that spikes of electrical activity are used by fungi to communicate and process information in mycelium networks, we group spikes into words and provide a linguistic and information complexity analysis of the fungal spiking activity. We demonstrate that distributions of fungal word lengths match that of human languages. We also construct algorithmic and Liz-Zempel complexity hierarchies of fungal sentences and show that species <jats:italic>S. commune</jats:italic> generate the most complex sentences. </jats:p>

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