Skip to main content
SpringerLink
Log in

Language of Fungi Derived from their Electrical Spiking Activity

  • Chapter
  • First Online:
Fungal Machines

Part of the book series: Emergence, Complexity and Computation ((ECC,volume 47))

  • 260 Accesses

Abstract

Fungi exhibit oscillations of extracellular electrical potential recorded via differential electrodes inserted into a substrate colonised by mycelium or directly into sporocarps. We analysed electrical activity of ghost fungi (Omphalotus nidiformis), Enoki fungi (Flammulina velutipes), split gill fungi (Schizophyllum commune) and caterpillar fungi (Cordyceps militaris). The spiking characteristics are species specific: a spike duration varies from one to 21 h and an amplitude from 0.03 mV to 2.1mV. 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 S. commune generate the most complex sentences.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://complexitycalculator.com/index.html.

References

  1. Baslow, M.H.: The languages of neurons: an analysis of coding mechanisms by which neurons communicate, learn and store information. Entropy. 11(4), 782–797 (2009)

    Google Scholar 

  2. Andres, D.S.: The language of neurons: theory and applications of a quantitative analysis of the neural code. Int. J. Med. Biol. Front. 21(2), 133 (2015)

    Google Scholar 

  3. Pruszynski, J.A., Zylberberg, J.: The language of the brain: real-world neural population codes. Curr. Opin. Neurobiol. 58, 30–36 (2019)

    Google Scholar 

  4. Eckert, R., Naitoh, Y., Friedman, K.: Sensory mechanisms in paramecium. i. J. Exp. Biol. 56, 683–694 (1972)

    Google Scholar 

  5. Bingley, M.S.: Membrane potentials in amoeba Proteus. J. Exp. Biol. 45(2), 251–267 (1966)

    Article  Google Scholar 

  6. Ooyama, S., Shibata, T.: Hierarchical organization of noise generates spontaneous signal in paramecium cell. J. Theor. Biol. 283(1), 1–9 (2011)

    Article  MATH  Google Scholar 

  7. Hanson, A.: Spontaneous electrical low-frequency oscillations: a possible role in hydra and all living systems. Philos. Trans. R. Soc. B. 376(1820), 20190763 (2021)

    Google Scholar 

  8. Iwamura, T.: Correlations between protoplasmic streaming and bioelectric potential of a slime mold. Physarum polycephalum. Shokubutsugaku Zasshi 62(735–736), 126–131 (1949)

    Article  Google Scholar 

  9. Kamiya, N., Abe, S.: Bioelectric phenomena in the myxomycete plasmodium and their relation to protoplasmic flow. J. Colloid Sci. 5(2), 149–163 (1950)

    Article  Google Scholar 

  10. Trebacz, K., Dziubinska, H., Krol, E.: Electrical signals in long-distance communication in plants. In: Communication in Plants, pp. 277–290. Springer (2006)

    Google Scholar 

  11. Fromm, J., Lautner, S.: Electrical signals and their physiological significance in plants. Plant Cell & Environ. 30(3), 249–257 (2007)

    Article  Google Scholar 

  12. Zimmermann, M.R., Mithöfer, A.: Electrical long-distance signaling in plants. In: Long-Distance Systemic Signaling and Communication in Plants, pp. 291–308. Springer (2013)

    Google Scholar 

  13. Slayman, C.L., Long, W.S., Gradmann, D.: “Action potentials” in Neurospora crassa, a mycelial fungus. Biochimica et Biophysica Acta (BBA)—Biomembranes. 426(4), 732–744 (1976)

    Google Scholar 

  14. Olsson, S., Hansson, B.S.: Action potential-like activity found in fungal mycelia is sensitive to stimulation. Naturwissenschaften 82(1), 30–31 (1995)

    Article  Google Scholar 

  15. Adamatzky, A.: On spiking behaviour of oyster fungi Pleurotus djamor. Sci. Rep. 8(1), 1–7 (2018)

    Article  MathSciNet  Google Scholar 

  16. Adamatzky, A., Gandia, A.: On electrical spiking of Ganoderma resinaceum. Biophys. Rev. Lett. :1–9

    Google Scholar 

  17. Cocatre-Zilgien, J.H., Delcomyn, F.: Identification of bursts in spike trains. J. Neurosci. Methods. 41(1), 19–30 (1992)

    Article  Google Scholar 

  18. Legendy, C.R., Salcman, M.: Bursts and recurrences of bursts in the spike trains of spontaneously active striate cortex neurons. J. Neurophysiol. 53(4), 926–939 (1985)

    Article  Google Scholar 

  19. Adamatzky, A., Gandia, A., Chiolerio, A.: Fungal sensing skin. Fungal Biol. Biotechnol. 8(1), 1–6 (2021)

    Google Scholar 

  20. Adamatzky, A., Nikolaidou, A., Gandia, A., Chiolerio, A., Dehshibi, M.M.: Reactive fungal wearable. Biosyst. 199, 104304 (2021)

    Google Scholar 

  21. Berbara, R.L.L., Morris, B.M., Fonseca, H.M.A.C., Reid, B., Gow, N.A.R., Daft, M.J.: Electrical currents associated with arbuscular mycorrhizal interactions. New Phytol. 129(3), 433–438 (1995)

    Article  Google Scholar 

  22. Dehshibi, M.M., Adamatzky, A.: Electrical activity of fungi: spikes detection and complexity analysis. Biosyst. 203, 104373 (2021)

    Google Scholar 

  23. Witzany, G., Nowacki, M.: Biocommunication of Ciliates, vol. 372. Springer (2016)

    Google Scholar 

  24. Witzany,G.: Bio-communication of plants. Nat. Preced. 1 (2007)

    Google Scholar 

  25. Witzany, G., Baluška, F.: Biocommunication of Plants, vol. 14. Springer Science & Business Media (2012)

    Google Scholar 

  26. Šimpraga, M., Takabayashi, J., Holopainen, J.K.: Language of plants: where is the word?. J. Integr. Plant Biol. 58(4), 343–349 (2016)

    Google Scholar 

  27. Trewavas, A.: Intelligence, cognition, and language of green plants. Front. Psychol. 7, 588 (2016)

    Google Scholar 

  28. Gagliano, M., Grimonprez, M.: Breaking the silence-language and the making of meaning in plants. Ecopsychology. 7(3), 145–152 (2015)

    Article  Google Scholar 

  29. Marler, P., Griffin, D.R.: The 1973 nobel prize for physiology or medicine. Sci. 182(4111), 464–466 (1973)

    Google Scholar 

  30. Von Frisch, K.: Bees: Their Vision, Chemical Senses, and Language. Cornell University Press (2014)

    Google Scholar 

  31. Hölldobler, B.: Communication between ants and their guests. Sci. Am. 224(3), 86–95 (1971)

    Article  Google Scholar 

  32. Reznikova, Z.I., Ryabko, B.Y.: Analysis of the language of ants by information-theoretical methods. Problemy Peredachi Informatsii. 22(3), 103–108 (1986)

    Google Scholar 

  33. Reznikova, Z.I., Ryabko, B.Y.: Experimental proof of the use of numerals in the language of ants. Problemy Peredachi Informatsii. 24(4), 97–101 (1988)

    Google Scholar 

  34. Ryabko, B., Reznikova, Z.: Using Shannon entropy and Kolmogorov complexity to study the communicative system and cognitive capacities in ants. Complex. 2(2), 37–42 (1996)

    Article  Google Scholar 

  35. Ryabko, B., Reznikova, Z.: The use of ideas of information theory for studying “language’’ and intelligence in ants. Entropy. 11(4), 836–853 (2009)

    Article  Google Scholar 

  36. Reznikova, Z., Ryabko, B.: Ants and bits. IEEE Inf. Theory Soc. Newsl. 62(5), 17–20 (2012)

    Google Scholar 

  37. Lee, R., Jonathan, P., Ziman, P.: Pictish symbols revealed as a written language through application of Shannon entropy. Proc. R. Soc. Math. Phys. Eng. Sci. 466(2121), 2545–2560 (2010)

    MATH  Google Scholar 

  38. Sigurd, B., Eeg-Olofsson, M., Van Weijer, J.: Word length, sentence length and frequency-Zipf revisited. Stud. Linguist. 58(1), 37–52 (2004)

    Article  Google Scholar 

  39. Bochkarev, V.V., Shevlyakova, A.V., Solovyev, V.D.: The average word length dynamics as an indicator of cultural changes in society. Soc. Evol. & Hist. 14(2), 153–175 (2015)

    Google Scholar 

  40. Hatzigeorgiu, N., Mikros, G., Carayannis, G.: Word length, word frequencies and Zipf’s law in the Greek language. J. Quant. Linguist. 8(3), 175–185 (2001)

    Article  Google Scholar 

  41. House, A.S.: On vowel duration in English. J. Acoust. Soc. Am. 33(9), 1174–1178 (1961)

    Google Scholar 

  42. Weber-Fox, C.M., Neville. H.J.: Functional neural subsystems are differentially affected by delays in second language immersion: ERP and behavioral evidence in bilinguals. In: Second Language Acquisition and the Critical Period Hypothesis, p. 2338 (1999)

    Google Scholar 

  43. Kamada, T., Kawai, S.: An algorithm for drawing general undirected graphs. Inf. Process. Lett. 31(1), 7–15 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  44. Zenil, H.: A review of methods for estimating algorithmic complexity: Options, challenges, and new directions. Entropy. 22(6), 612 (2020)

    Google Scholar 

  45. Lin, J.: Divergence measures based on the Shannon entropy. IEEE Trans. Inf. Theory. 37(1), 145–151 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  46. Ziv, J., Lempel, A.: A universal algorithm for sequential data compression. IEEE Trans. Inf. Theory. 23(3), 337–343 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  47. Ziv, J., Lempel, A.: Compression of individual sequences via variable-rate coding. IEEE Trans. Inf. Theory 24(5), 530–536 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  48. Zenil, H., Hernández-Orozco, S., Kiani, N.A., Soler-Toscano, F., Rueda-Toicen, A., Tegnér, J.: A decomposition method for global evaluation of Shannon entropy and local estimations of algorithmic complexity. Entropy. 20(8), 605 (2018)

    Google Scholar 

  49. Gauvrit, N., Zenil, H., Delahaye, J.-P., Soler-Toscano, F.: Algorithmic complexity for short binary strings applied to psychology: a primer. Behav. Res. Methods 46(3), 732–744 (2014)

    Article  Google Scholar 

  50. Delahaye, J.-P., Zenil, H.: Numerical evaluation of algorithmic complexity for short strings: a glance into the innermost structure of randomness. Appl. Math. Comput. 219(1), 63–77 (2012)

    MATH  Google Scholar 

  51. Kepecs, A., Lisman, J.: Information encoding and computation with spikes and bursts. Netw. Comput. Neural Syst. 14(1), 103 (2003)

    Google Scholar 

  52. Gabbiani, F., Metzner, W.: Encoding and processing of sensory information in neuronal spike trains. J. Exp. Biol. 202(10), 1267–1279 (1999)

    Google Scholar 

  53. Carandini, M., Mechler, F., Leonard, C.S., Movshon, J.A.: Spike train encoding by regular-spiking cells of the visual cortex. J. Neurophysiol. 76(5), 3425–3441 (1996)

    Google Scholar 

  54. Gabbiani, F., Koch, C.: Principles of spike train analysis. Methods Neuronal Model. 12(4), 313–360 (1998)

    Google Scholar 

  55. Draper, T.C., Dueñas-Díez, M., Pérez-Mercader, J.: Exploring the symbol processing ‘time interval’parametric constraint in a Belousov–Zhabotinsky operated chemical turing machine. RSC Adv. 11(37), 23151–23160 (2021)

    Google Scholar 

  56. Pier Luigi Gentili: Establishing a new link between fuzzy logic, neuroscience, and quantum mechanics through Bayesian probability: perspectives in artificial intelligence and unconventional computing. Mol. 26(19), 5987 (2021)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Unconventional Computing Laboratory, UWE, Bristol, UK

    Andrew Adamatzky

Authors
  1. Andrew Adamatzky
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrew Adamatzky .

Editor information

Editors and Affiliations

  1. Unconventional Computing Laboratory, University of the West of England, Bristol, UK

    Andrew Adamatzky

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Adamatzky, A. (2023). Language of Fungi Derived from their Electrical Spiking Activity. In: Adamatzky, A. (eds) Fungal Machines. Emergence, Complexity and Computation, vol 47. Springer, Cham. https://doi.org/10.1007/978-3-031-38336-6_25

Download citation

Publish with us

Policies and ethics

Search

Navigation