An Approach to Building Musical Bioprocessors with Physarum polycephalum Memristors

Chapter

Abstract

This chapter presents an account of our investigation into developing musical processing devices using biological components. Such work combines two vibrant areas of unconventional computing research: Physarum polycephalum and the memristor. P. polycephalum is a plasmodial slime mould that has been discovered to display behaviours that are consistent with that of the memristor: a hybrid memory and processing component. Within the chapter, we introduce the research’s background and our motives for undertaking the study. Then, we demonstrate P. polycephalum’s memristive abilities and present our approach to enabling its integration into analogue circuitry. Following on, we discuss different techniques for using P. polycephalum memristors to generate musical responses.

References

  1. Adamatzky, A. (2010). Physarum machines: Computers from slime mould, Vol. 74. World Scientific.Google Scholar
  2. Adamatzky, A. (2012). Bioevaluation of world transport networks. World Scientific.Google Scholar
  3. Adamatzky, A. (2013). Towards slime mould colour sensor: Recognition of colours by Physarum polycephalum. Organic Electronics, 14(12), 3355–3361.CrossRefGoogle Scholar
  4. Adamatzky, A. (2015). Thirty eight things to do with live slime mould. arXiv preprint arXiv:1512.08230
  5. Adamatzky, A., de Lacy Costello, B., Melhuish, C., & Ratcliffe, N. (2003). Experimental reaction–diffusion chemical processors for robot path planning. Journal of Intelligent and Robotic Systems, 37(3), 233–249.CrossRefMATHGoogle Scholar
  6. Adamatzky, A., & Jones, J. (2011). On electrical correlates of Physarum polycephalum spatial activity: Can we see physarum machine in the dark? Biophysical Reviews and Letters, 6(01n02), 29–57.Google Scholar
  7. Adamatzky, A., Jones, J., Mayne, R., Tsuda, S., & Whiting, J. (2016). Logical gates and circuits implemented in slime mould. In Advances in Physarum Machines. Springer, pp. 37–74.Google Scholar
  8. Adamatzky, A., & Schubert, T. (2014). Slime mold microfluidic logical gates. Materials Today, 17(2), 86–91.CrossRefGoogle Scholar
  9. Braund, E., & Miranda, E. (2015a). Biocomputer music: Generating musical responses with Physarum polycephalum-based memristors. Computer Music Multidisciplinary Research (CMMR): Music, Mind and Embodiment. Plymouth, UK.Google Scholar
  10. Braund, E., & Miranda, E. (2015b). Music with unconventional computing: Towards a step sequencer from plasmodium of Physarum polycephalum. In Evolutionary and Biologically Inspired Music, Sound, Art and Design. Springer, pp. 15–26.Google Scholar
  11. Braund, E., & Miranda, E. (In Press). On building practical biocomputers for real-world applications: Receptacles for culturing slime mould memristors and component standardisation. Journal of Bionic Engineering.Google Scholar
  12. Braund, E., Sparrow, R., & Miranda, E. (2016). Physarum-based memristors for computer music. In Advances in Physarum Machines. Springer, pp. 755–775.Google Scholar
  13. Chua, L. O. (1971). Memristor-the missing circuit element. IEEE Transactions on Circuit Theory, 18(5), 507–519.CrossRefGoogle Scholar
  14. Chua, L. O. (2015). Everything you wish to know about memristors but are afraid to ask. Radioengineering, 24(2), 319.CrossRefGoogle Scholar
  15. Coggin, S. J., & Pazun, J. L. (1996). Dynamic complexity in Physarum polycephalum shuttle streaming. Protoplasma, 194(3–4), 243–249.CrossRefGoogle Scholar
  16. Doornbusch, P. (2009). The Oxford handbook of computer music, Oxford University Press, chapter Early Hardware and Easy Ideas in Computer Music: Their Development and Their Current Forms.Google Scholar
  17. Gale, E., Adamatzky, A., & Costello, B. (2013a). Slime mould memristors. BioNanoScience, 5(1), 1–8.CrossRefGoogle Scholar
  18. Gale, E., Costello, B., & Adamatzky, A. (2014). Spiking in memristor networks. Cham: Springer, pp. 365–387. http://dx.doi.org/10.1007/978-3-319-02630-5_17
  19. Gale, E., Matthews, O., Costello, B. D. L., & Adamatzky, A. (2013). Beyond markov chains, towards adaptive memristor network-based music generation. arXiv preprint arXiv:1302.0785
  20. Gotoh, K., & Kuroda, K. (1982). Motive force of cytoplasmic streaming during plasmodial mitosis of Physarum polycephalum. Cell Motility, 2(2), 173–181.CrossRefGoogle Scholar
  21. Gupta, B., Revagade, N., & Hilborn, J. (2007). Poly (lactic acid) fiber: An overview. Progress in Polymer Science, 32(4), 455–482.CrossRefGoogle Scholar
  22. Guy, R. D., Nakagaki, T., & Wright, G. B. (2011). Flow-induced channel formation in the cytoplasm of motile cells. Physical Review E, 84(1), 016310.CrossRefGoogle Scholar
  23. Howard, G., Gale, E., Bull, L., de Lacy Costello, B., & Adamatzky, A. (2012). Evolution of plastic learning in spiking networks via memristive connections. IEEE Transactions on Evolutionary Computation, 16(5), 711–729.CrossRefGoogle Scholar
  24. Linares-Barranco, B., & Serrano-Gotarredona, T. (2009). Memristance can explain spike-time-dependent-plasticity in neural synapses. Nature precedings, 1, 2009.Google Scholar
  25. Miranda, E. Biocomputer music. http://tinyurl.com/kszgm3r. Last Accessed February 12, 2015.
  26. Miranda, E. R. (2000). Readings in music and artificial intelligence, Vol. 20. Routledge.Google Scholar
  27. Nakagaki, T., Yamada, H., & Tóth, Á. (2000). Intelligence: Maze-solving by an amoeboid organism. Nature, 407(6803), 470–470.CrossRefGoogle Scholar
  28. Pershin, Y. V., Di La Fontaine, S., & Ventra, M. (2009). Memristive model of amoeba learning. Physical Review E, 80(2), 021926.CrossRefGoogle Scholar
  29. Romeo, A., Dimonte, A., Tarabella, G., D’Angelo, P., Erokhin, V., & Iannotta, S. (2015). A bio-inspired memory device based on interfacing Physarum polycephalum with an organic semiconductor. APL materials, 3(1), 014909.CrossRefGoogle Scholar
  30. Saigusa, T., Tero, A., Nakagaki, T., & Kuramoto, Y. (2008). Amoebae anticipate periodic events. Physical Review Letters, 100(1), 018101.CrossRefGoogle Scholar
  31. Schuster, A., & Yamaguchi, Y. (2011). From foundational issues in artificial intelligence to intelligent memristive nano-devices. International Journal of Machine Learning and Cybernetics, 2(2), 75–87.CrossRefGoogle Scholar
  32. Shu, J.-J., Wang, Q.-W., Yong, K.-Y., Shao, F., & Lee, K. J. (2015). Programmable dna-mediated multitasking processor. The Journal of Physical Chemistry B, 119(17), 5639–5644.CrossRefGoogle Scholar
  33. Snider, G. S. (2008). Spike-timing-dependent learning in memristive nanodevices. In 2008 IEEE international symposium on nanoscale architectures (pp. 85–92). IEEE.Google Scholar
  34. Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008). The missing memristor found. Nature, 453(7191), 80–83.CrossRefGoogle Scholar
  35. Tarabella, G., D’Angelo, P., Cifarelli, A., Dimonte, A., Romeo, A., Berzina, T., et al. (2015). A hybrid living/organic electrochemical transistor based on the Physarum polycephalum cell endowed with both sensing and memristive properties. Chemical Science, 6(5), 2859–2868.CrossRefGoogle Scholar
  36. Tsuda, S., Zauner, K.-P., & Gunji, Y.-P. (2007). Robot control with biological cells. Biosystems, 87(2), 215–223.CrossRefGoogle Scholar
  37. Versace, M., & Chandler, B. (2010). The brain of a new machine. IEEE Spectrum, 47(12), 30–37.CrossRefGoogle Scholar
  38. Whiting, J. G., Costello, B. P., & Adamatzky, A. (2014). Slime mould logic gates based on frequency changes of electrical potential oscillation. Biosystems, 124, 21–25.CrossRefGoogle Scholar
  39. Wohlfarth-Bottermann, K. (1979). Oscillatory contraction activity in physarum. The Journal of experimental biology, 81(1), 15–32.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Interdisciplinary Centre for Computer Music Research (ICCMR)Plymouth UniversityPlymouthUK

Personalised recommendations