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.
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Notes
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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.
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We used available service at https://www.random.org/.
References
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)
Eckert, R., Brehm, P.: Ionic mechanisms of excitation in paramecium. Annu. Rev. Biophys. Bioeng. 8(1), 353–383 (1979)
Hansma, H.G.: Sodium uptake and membrane excitation in paramecium. J. Cell Biol. 81(2), 374–381 (1979)
Bingley, M.S.: Membrane potentials in amoeba proteus. J. Exp. Biol. 45(2), 251–267 (1966)
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)
Trebacz, K., Dziubinska, H., Krol, E.: Electrical signals in long-distance communication in plants. In: Communication in Plants, pp. 277–290. Springer, Berlin (2006)
Fromm, J., Lautner, S.: Electrical signals and their physiological significance in plants. Plant, Cell Environ. 30(3), 249–257 (2007)
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)
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)
Aidley, D.J., Ashley, D.J.: The Physiology of Excitable Cells, vol. 4. Cambridge University Press Cambridge, Cambridge (1998)
Nelson, P.G., Lieberman, M.: Excitable Cells in Tissue Culture. Springer Science & Business Media (2012)
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)
Kittel, Ch.: Excitation of spin waves in a ferromagnet by a uniform RF field. Phys. Rev. 110(6), 1295 (1958)
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)
Slonczewski, J.C.: Excitation of spin waves by an electric current. J. Magn. Magn. Mater. 195(2), L261–L268 (1999)
Gorbunov, L.M., Kirsanov, V.I.: Excitation of plasma waves by an electromagnetic wave packet. Sov. Phys. JETP 66(290–294), 40 (1987)
Belousov, B.P.: A periodic reaction and its mechanism. Compil. Abstr. Radiat. Med. 147(145), 1 (1959)
Zhabotinsky, A.M.: Periodic processes of malonic acid oxidation in a liquid phase. Biofizika 9(306–311), 11 (1964)
Zhabotinsky, A.M.: Belousov-zhabotinsky reaction. Scholarpedia 2(9), 1435 (2007)
Farkas, I., Helbing, D., Vicsek, T.: Social behaviour: Mexican waves in an excitable medium. Nature 419(6903), 131 (2002)
Farkas, I., Helbing, D., Vicsek, T.: Human waves in stadiums. Phys. A: Stat. Mech. Its Appl. 330(1–2), 18–24 (2003)
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)
Adamatzky, A.: Plant leaf computing. Biosystems (2019)
Adamatzky, A., Nikolaidou, A., Gandia, A., Chiolerio, A., Dehshibi, M.M.: Reactive fungal wearable. Biosystems 199, 104304 (2020)
Nenadic, Z., Burdick, J.W.: Spike detection using the continuous wavelet transform. IEEE Trans. Biomed. Eng. 52(1), 74–87 (2004)
Shimazaki, H., Shinomoto, S.: Kernel bandwidth optimization in spike rate estimation. J. Comput. Neurosci. 29(1–2), 171–182 (2010)
Vicnesh, J., Hagiwara, Y.: Accurate detection of seizure using nonlinear parameters extracted from EEG signals. J. Mech. Med. Biol. 19(01), 1940004 (2019)
Adamatzky, A., Gandia, A.: On electrical spiking of ganoderma resinaceum. Biophys. Rev. Lett. 1–9 (2021)
Lilly, J.M., Olhede, S.C.: Generalized morse wavelets as a superfamily of analytic wavelets. IEEE Trans. Signal Process. 60(11), 6036–6041 (2012)
IEEE standard for transitions, pulses, and related waveforms. IEEE Std 181-2011 (Revision of IEEE Std 181-2003), pp, 1–71 (2011)
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)
Lilly, J.M., Olhede, S.C.: Higher-order properties of analytic wavelets. IEEE Trans. Signal Process. 57(1), 146–160 (2008)
Marple, L.: Computing the discrete-time analytic signal via FFT. IEEE Trans. Signal Process. 47(9), 2600–2603 (1999)
Adamatzky, A.: On spiking behaviour of oyster fungi pleurotus djamor. Sci. Rep. 8(1), 1–7 (2018)
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)
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)
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)
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)
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)
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)
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)
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)
Obeid, I., Wolf, P.D.: Evaluation of spike-detection algorithms fora brain-machine interface application. IEEE Trans. Biomed. Eng. 51(6), 905–911 (2004)
Wilson, S.B., Emerson, R.: Spike detection: a review and comparison of algorithms. Clin. Neurophysiol. 113(12), 1873–1881 (2002)
Gotman, J., Wang, L.Y.: State-dependent spike detection: concepts and preliminary results. Electroencephalogr. Clin. Neurophysiol. 79(1), 11–19 (1991)
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)
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)
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)
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)
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)
Liu, Z., Wang, X., Yuan, Q.: Robust detection of neural spikes using sparse coding based features. Math. Biosci. Eng. 17(4), 4257 (2020)
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
Adamatzky, A.: Tactile bristle sensors made with slime mold. IEEE Sens. J. 14(2), 324–332 (2013)
Deutsch, P., Gailly, J.: Zlib compressed data format specification version 3.3. Technical report, RFC 1950 (1996)
Howard, P.G.: The Design and Analysis of Efficient Lossless Data Compression Systems. Ph.D. thesis, Citeseer (1993)
Roelofs, G., Koman, R.: PNG: The Definitive Guide. O’Reilly & Associates, Inc. (1999)
Ziv, J., Lempel, A.: A universal algorithm for sequential data compression. IEEE Trans. Inf. Theory 23(3), 337–343 (1977)
Kaspar, F., Schuster, H.G.: Easily calculable measure for the complexity of spatiotemporal patterns. Phys. Rev. A 36(2), 842 (1987)
Huffman, D.A.: A method for the construction of minimum-redundancy codes. Proc. IRE 40(9), 1098–1101 (1952)
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)
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)
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)
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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
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