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Using the fungal electrical activity for computing

  Pleurotus djamor

Fungal mycelium like Pleurotus djamor can resolve an incredible range of computational geometry problems (photo: Rachel Horton /

Santiago Campillo
This research investigated the information-theoretic complexity of fungal electrical activity to pave the way for additional investigation into sensorial fusion and fungi decision making

Materials have a variety of properties that can be used to solve computational problems, according to studies in substrate-based computing. BZ computers, slime mould computers, plant computers, and collision-based liquid marbles computers are just a few examples of prototypes produced for future and emergent computing devices. Modelling the computational processes that exist in such systems, however, is a difficult task in general.

Claiming that fungi are the most intelligent living organisms in the world sounds like an exaggeration. However, a recent study by Prof. Andrew Adamatzky of the Unconventional Computing Laboratory at the UWE Bristol, who is the Principal Investigator of the FUNGAR project, and Mohammad Mahdi Dehshibi of the Scene Understanding and Artificial Intelligence Lab (SUNAI) group at the UOC Faculty of Computer Science, Multimedia and Telecommunications concurs with this idea. Its implications are numerous and practical in both the medium and the long term.


Converting the fungal electrical signals into messages

Fungal mycelium like Pleurotus djamor, also known as the pink oyster mushroom, can resolve an incredible range of computational geometry problems, explained researchers in a previously published article on fungal materials. “By changing the environmental conditions, we can reprogramme a geometry and a theoretical structure of the graphics of mycelium networks and then use the electrical activity of the fungi to create computing circuits”, confirmed the researcher. 

In a recent study, Electrical activity of fungi: Spikes detection and complexity analysis, the researchers demonstrate that the Oyster fungi Pleurotus djamor generate actin potential like spikes of electrical potential. The researchers’ proposal consists of a method for detecting spike arrival time through an exhaustive algorithm that enables a relatively efficient characterization of the electrical activity. The authors measure the “complexity” of these spikes. They speculate that the complexity of fungal language is higher than that of human languages (at least for European languages). The results can pave the ways for future research on the sensorial fusion of fungi. 

“At the moment, there are two major challenges to be confronted [in being able to use fungi as computers]”, explained the researchers. “The first is to implement a computing purpose that makes sense. The second is to characterize the properties of the fungal substrates to discover their true computational potential”. These two steps are essential for building functional computing units.


Designing environmental sensors

Will we really see, then, a laptop computer with a microprocessor made with fungi? For the author, the objective of fungal computers is not to replace silicon chips, as the actions in this type of computer are too slow for that. However, the properties of fungi could be used as an “environmental sensor on a large scale”. Fungal networks could monitor large quantities of data flows as part of their day-to-day activity. If we were able to connect to their networks and interpret the signals they use to process information, we could learn more about what is happening in an ecosystem and act accordingly. 


This research project is in line with Sustainable Development Goal (SDG) 9, to build resilient infrastructure, promote sustainable industrialization and foster innovation.


Related paper

Mohammad Mahdi Dehshibi, Andrew Adamatzky. “Electrical activity of fungi: Spikes detection and complexity analysis”. Biosystems (2021).



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Mohammad Mahdi Dehshibi

Mohammad Mahdi Dehshibi

SUNAI researcher, Faculty of Computer Science, Multimedia and Telecommunications

Expert in: Computer science

Knowledge area:

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