Neurogranular Synthesis: Granular Synthesis Controlled by a Pulse-Coupled Network of Spiking Neurons

  • Kevin McCracken
  • John Matthias
  • Eduardo Miranda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6625)


We introduce a method of generating grain parameters of a granular synthesiser in real-time by using a network of artificial spiking neurons, the behaviour of which is determined by user-control of a small number of network parameters; ‘Neurogranular synthesis’. The artificial network can exhibit a wide variety of behaviour from loosely correlated to highly synchronised, which can produce interesting sonic results, particularly with regard to rhythmic textures.


Spiking neurons granular synthesis interactive musical control systems 


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  1. 1.
    Bi, G., Poo, M.: Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type. Journal of Neuroscience 18(24), 10464–10472 (1998)Google Scholar
  2. 2.
    Bokesoy, S., Pape, G.: Stochos: Software for Real-Time Synthesis of Stochastic Music. Computer Music Journal 27(3), 33–43 (2003)CrossRefGoogle Scholar
  3. 3.
    Ferguson, E.: The Future of Music? It might be... The Observer, London (February 25, 2009)Google Scholar
  4. 4.
    Gabor, D.: Acoustical Quanta and the Theory of Hearing. Nature 159(4044), 591–594 (1947)CrossRefGoogle Scholar
  5. 5.
    Grant, J., Matthias, J.R., Ryan, N., Jones, D., Hodgson, T., Outram, N.: The Fragmented Orchestra (2009),
  6. 6.
    Hickling, A.: Noises Off, The Guardian (December 19, 2008)Google Scholar
  7. 7.
    Hodgkin, A.L., Huxley, A.F.: A Quantitative Description of Ion Currents and its Applications to Conduction and Excitation in Nerve Membranes. J. Physiol (Lond.) 117, 500–544 (1952)CrossRefGoogle Scholar
  8. 8.
    Izhikevich, E.M.: A Simple Model of Spiking Neurons. IEEE Transactions on Neural Networks 14, 1469 (2003)CrossRefGoogle Scholar
  9. 9.
    Izhikevich, E.M.: Which Model to Use for Cortical Spiking Neurons? IEEE Transactions on Neural Networks 15, 1063–1070 (2004)CrossRefGoogle Scholar
  10. 10.
    Izhikevich, E.M., Gally, J.A., Edelman, G.M.: Spike-timing Dynamics of Neuronal Groups. Cerebral Cortex 14, 933–944 (2004)CrossRefGoogle Scholar
  11. 11.
    Lubenov, E.V., Siapas, A.G.: Neuron.  58, 118–131 (2008)Google Scholar
  12. 12.
    Matthias, J.R., Ryan, N.: Cortical Songs. CD. Nonclassical Records, London (2008)Google Scholar
  13. 13.
    Miranda, E.R., Wanderley, M.M.: New Digital Musical Instruments: Control and Interaction Beyond the Keyboard. The Computer Music and Digital Audio Series. A-R Editions, Middleton (2006)Google Scholar
  14. 14.
    Miranda, E.R., Matthias, J.R.: Music Neurotechnology for Sound Synthesis. Leonardo 42, 439–442 (2009)CrossRefGoogle Scholar
  15. 15.
    Morrison, R.: Liverpool ends year on a cultural high with The Fragmented Orchestra. The Times, London (2008)Google Scholar
  16. 16.
    Murray, J., Miranda, E.R., Matthias, J.: Real-time Granular Synthesis with Spiking Neurons. In: Proceedings of Consciousness Reframed - 8th International Conference. University of Plymouth, Plymouth (2006) (invited talk) Google Scholar
  17. 17.
    Roads, C.: Microsound, pp. 14–16. MIT Press, Cambridge (2004)Google Scholar
  18. 18.
    Roads, C.: Microsound, pp. 383–388. MIT Press, Cambridge (2004)Google Scholar
  19. 19.
    Truax, B.: Real-Time Granular Synthesis with the DMX-100. Computer Music Journal 12(2) (1988)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Kevin McCracken
    • 1
  • John Matthias
    • 1
  • Eduardo Miranda
    • 1
  1. 1.Faculty of ArtsUniversity of PlymouthUK

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