Skip to main content

Complexity of Electrical Spiking of Fungi

  • Chapter
  • First Online:
Fungal Machines

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

  • 280 Accesses

Abstract

Oyster fungi Pleurotus djamor generate action 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 by fungi.

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

Access this chapter

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

Notes

  1. 1.

    Calling the spikes spontaneous means that the intentional external stimulus does not invoke them. Otherwise, the spikes actually reflect the ongoing physiological and morphological processes in the mycelial networks.

  2. 2.

    We observed in our previous studies [22, 23] that minimum spike length was 5 mins.

  3. 3.

    https://www.sciencemag.org/news/2020/07/meet-lizard-man-reptile-loving-biologist-tackling-some-biggest-questions-evolution.

  4. 4.

    We used available service at https://www.random.org/.

References

  1. Masi, E., Ciszak, M., Santopolo, L., Frascella, A., Giovannetti, L., Marchi, E., Viti, C., Mancuso, S.: Electrical spiking in bacterial biofilms. J. R. Soc. Interface 12(102), 20141036 (2015)

    Article  Google Scholar 

  2. Eckert, R., Brehm, P.: Ionic mechanisms of excitation in paramecium. Annu. Rev. Biophys. Bioeng. 8(1), 353–383 (1979)

    Article  Google Scholar 

  3. Hansma, H.G.: Sodium uptake and membrane excitation in paramecium. J. Cell Biol. 81(2), 374–381 (1979)

    Google Scholar 

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

    Article  Google Scholar 

  5. McGillviray, A.N., Gow, N.A.R.: The transhyphal electrical current of N euruspua crassa is carried principally by protons. Microbiology 133(10), 2875–2881 (1987)

    Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

  9. Hodgkin, A.L., Huxley, A.F.: A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117(4), 500–544 (1952)

    Google Scholar 

  10. Aidley, D.J., Ashley, D.J.: The Physiology of Excitable Cells, vol. 4. Cambridge University Press Cambridge, Cambridge (1998)

    Google Scholar 

  11. Nelson, P.G., Lieberman, M.: Excitable Cells in Tissue Culture. Springer Science & Business Media (2012)

    Google Scholar 

  12. Davidenko, J.M., Pertsov, A.V., Salomonsz, R., Baxter, W., Jalife, J.: Stationary and drifting spiral waves of excitation in isolated cardiac muscle. Nature 355(6358), 349 (1992)

    Google Scholar 

  13. Kittel, Ch.: Excitation of spin waves in a ferromagnet by a uniform RF field. Phys. Rev. 110(6), 1295 (1958)

    Article  MathSciNet  MATH  Google Scholar 

  14. Tsoi, M., Jansen, A.G.M., Bass, J., Chiang, W.-C., Seck, M., Tsoi, V., Wyder, P.: Excitation of a magnetic multilayer by an electric current. Phys. Rev. Lett. 80(19), 4281 (1998)

    Article  Google Scholar 

  15. Slonczewski, J.C.: Excitation of spin waves by an electric current. J. Magn. Magn. Mater. 195(2), L261–L268 (1999)

    Article  Google Scholar 

  16. Gorbunov, L.M., Kirsanov, V.I.: Excitation of plasma waves by an electromagnetic wave packet. Sov. Phys. JETP 66(290–294), 40 (1987)

    Google Scholar 

  17. Belousov, B.P.: A periodic reaction and its mechanism. Compil. Abstr. Radiat. Med. 147(145), 1 (1959)

    Google Scholar 

  18. Zhabotinsky, A.M.: Periodic processes of malonic acid oxidation in a liquid phase. Biofizika 9(306–311), 11 (1964)

    Google Scholar 

  19. Zhabotinsky, A.M.: Belousov-zhabotinsky reaction. Scholarpedia 2(9), 1435 (2007)

    Google Scholar 

  20. Farkas, I., Helbing, D., Vicsek, T.: Social behaviour: Mexican waves in an excitable medium. Nature 419(6903), 131 (2002)

    Article  Google Scholar 

  21. Farkas, I., Helbing, D., Vicsek, T.: Human waves in stadiums. Phys. A: Stat. Mech. Its Appl. 330(1–2), 18–24 (2003)

    Google Scholar 

  22. Adamatzky, A., Tuszynski, J., Pieper, J., Nicolau, D.V., Rinaldi, R., Sirakoulis, G.C., Erokhin, V., Schnauss, J., Smith, D.M.: Towards cytoskeleton computers: a proposal. In: Adamatzky, A., Akl, S. Sirakoulis, G.C. (eds.) From Parallel to Emergent Computing. CRC Group/Taylor & Francis (2019)

    Google Scholar 

  23. Adamatzky, A.: Plant leaf computing. Biosystems (2019)

    Google Scholar 

  24. Adamatzky, A., Nikolaidou, A., Gandia, A., Chiolerio, A., Dehshibi, M.M.: Reactive fungal wearable. Biosystems 199, 104304 (2020)

    Article  Google Scholar 

  25. Nenadic, Z., Burdick, J.W.: Spike detection using the continuous wavelet transform. IEEE Trans. Biomed. Eng. 52(1), 74–87 (2004)

    Google Scholar 

  26. Shimazaki, H., Shinomoto, S.: Kernel bandwidth optimization in spike rate estimation. J. Comput. Neurosci. 29(1–2), 171–182 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  27. Vicnesh, J., Hagiwara, Y.: Accurate detection of seizure using nonlinear parameters extracted from EEG signals. J. Mech. Med. Biol. 19(01), 1940004 (2019)

    Article  Google Scholar 

  28. Adamatzky, A., Gandia, A.: On electrical spiking of ganoderma resinaceum. Biophys. Rev. Lett. 1–9 (2021)

    Google Scholar 

  29. Lilly, J.M., Olhede, S.C.: Generalized morse wavelets as a superfamily of analytic wavelets. IEEE Trans. Signal Process. 60(11), 6036–6041 (2012)

    Google Scholar 

  30. IEEE standard for transitions, pulses, and related waveforms. IEEE Std 181-2011 (Revision of IEEE Std 181-2003), pp, 1–71 (2011)

    Google Scholar 

  31. Lilly, J.M.: Element analysis: a wavelet-based method for analysing time-localized events in noisy time series. Proc. R. Soc. A: Math., Phys. Eng. Sci. 473(2200), 20160776 (2017)

    Google Scholar 

  32. Lilly, J.M., Olhede, S.C.: Higher-order properties of analytic wavelets. IEEE Trans. Signal Process. 57(1), 146–160 (2008)

    Google Scholar 

  33. Marple, L.: Computing the discrete-time analytic signal via FFT. IEEE Trans. Signal Process. 47(9), 2600–2603 (1999)

    Article  MATH  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  35. Minoofam, S.A.H., Dehshibi, M.M., Bastanfard, A., Eftekhari, P.: Ad-hoc ma’qeli script generation using block cellular automata. J. Cell. Autom. 7(4), 321–334 (2012)

    Google Scholar 

  36. Minoofam, S.A.H., Dehshibi, M.M., Bastanfard, A., Shanbehzadeh, J.: Pattern formation using cellular automata and l-systems: a case study in producing islamic patterns. In: Cellular Automata in Image Processing and Geometry, pp. 233–252. Springer, Berlin (2014)

    Google Scholar 

  37. Parsa, S.S., Sourizaei, M., Dehshibi, M.M., Esmaeilzadeh Shateri, R., Parsaei, M.R.: Coarse-grained correspondence-based ancient Sasanian coin classification by fusion of local features and sparse representation-based classifier. Multimed. Tools Appl. 76(14), 15535–15560 (2017)

    Google Scholar 

  38. Taghipour, N., Javadi, H.H.S., Dehshibi, M.M., Adamatzky, A.: On complexity of persian orthography: L-systems approach. Complex Syst. 25(2), 127–156 (2016)

    Google Scholar 

  39. Dehshibi, M.M., Shirmohammadi, A., Adamatzky, A.: On growing persian words with l-systems: visual modeling of neyname. Int. J. Image Graph. 15(03), 1550011 (2015)

    Google Scholar 

  40. Dehshibi, M.M., Shanbehzadeh, J., Pedram, M.M.: A robust image-based cryptology scheme based on cellular nonlinear network and local image descriptors. Int. J. Parallel, Emergent Distrib. Syst. 35(5), 514–534 (2020)

    Google Scholar 

  41. Gholami, N., Dehshibi, M.M., Adamatzky, A., Rueda-Toicen, A., Zenil, H., Fazlali, M., Masip, D.: A novel method for reconstructing CT images in gate/geant4 with application in medical imaging: a complexity analysis approach. J. Inf. Process. 28, 161–168 (2020)

    Google Scholar 

  42. Quiroga, R.Q., Nadasdy, Z., Ben-Shaul, Y.: Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Comput. 16(8), 1661–1687 (2004)

    Google Scholar 

  43. Obeid, I., Wolf, P.D.: Evaluation of spike-detection algorithms fora brain-machine interface application. IEEE Trans. Biomed. Eng. 51(6), 905–911 (2004)

    Google Scholar 

  44. Wilson, S.B., Emerson, R.: Spike detection: a review and comparison of algorithms. Clin. Neurophysiol. 113(12), 1873–1881 (2002)

    Google Scholar 

  45. Gotman, J., Wang, L.Y.: State-dependent spike detection: concepts and preliminary results. Electroencephalogr. Clin. Neurophysiol. 79(1), 11–19 (1991)

    Article  Google Scholar 

  46. Wilson, S.B., Turner, C.A., Emerson, R.G., Scheuer, M.L.: Spike detection ii: automatic, perception-based detection and clustering. Clin. Neurophysiol. 110(3), 404–411 (1999)

    Google Scholar 

  47. Franke, F., Natora, M., Boucsein, C., Munk, M.H., Obermayer, K.: An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes. J. Comput. Neurosci. 29(1–2), 127–148 (2010)

    Google Scholar 

  48. Rácz, M., Liber, C., Németh, E., Fiáth, R., Rokai, J., Harmati, I., Ulbert, I., Márton, G.: Spike detection and sorting with deep learning. J. Neural Eng. 17(1), 016038 (2020)

    Article  Google Scholar 

  49. Wang, Z., Duanpo, W., Dong, F., Cao, J., Jiang, T., Liu, J.: A novel spike detection algorithm based on multi-channel of BECT EEG signals. In: Express Briefs, IEEE Transactions on Circuits and Systems II (2020)

    Google Scholar 

  50. Sablok, S., Gururaj, G., Shaikh, N., Shiksha, I., Choudhary, A.R.: Interictal spike detection in EEG using time series classification. In: 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 644–647. IEEE (2020)

    Google Scholar 

  51. Liu, Z., Wang, X., Yuan, Q.: Robust detection of neural spikes using sparse coding based features. Math. Biosci. Eng. 17(4), 4257 (2020)

    Article  MATH  Google Scholar 

  52. Dehshibi, M.M., Adamatzky, A.: Supplementary material for “Electrical activity of fungi: spikes detection and complexity analysis” 08 (2020). (Accessed on 24 Aug 2020). https://doi.org/10.5281/zenodo.3997031

  53. Adamatzky, A.: Tactile bristle sensors made with slime mold. IEEE Sens. J. 14(2), 324–332 (2013)

    Article  Google Scholar 

  54. Deutsch, P., Gailly, J.: Zlib compressed data format specification version 3.3. Technical report, RFC 1950 (1996)

    Google Scholar 

  55. Howard, P.G.: The Design and Analysis of Efficient Lossless Data Compression Systems. Ph.D. thesis, Citeseer (1993)

    Google Scholar 

  56. Roelofs, G., Koman, R.: PNG: The Definitive Guide. O’Reilly & Associates, Inc. (1999)

    Google Scholar 

  57. 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 

  58. Kaspar, F., Schuster, H.G.: Easily calculable measure for the complexity of spatiotemporal patterns. Phys. Rev. A 36(2), 842 (1987)

    Article  MathSciNet  Google Scholar 

  59. Huffman, D.A.: A method for the construction of minimum-redundancy codes. Proc. IRE 40(9), 1098–1101 (1952)

    Google Scholar 

  60. Huang, H., Lin, F.: A speech feature extraction method using complexity measure for voice activity detection in WGN. Speech Commun. 51(9), 714–723 (2009)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  62. Sadeniemi, M., Kettunen, K., Lindh-Knuutila, T., Honkela, T.: Complexity of European union languages: a comparative approach. J. Quant. Linguist. 15(2), 185–211 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andrew Adamatzky .

Editor information

Editors and Affiliations

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

Dehshibi, M.M., Adamatzky, A. (2023). Complexity of Electrical Spiking of Fungi. In: Adamatzky, A. (eds) Fungal Machines. Emergence, Complexity and Computation, vol 47. Springer, Cham. https://doi.org/10.1007/978-3-031-38336-6_4

Download citation

Publish with us

Policies and ethics