Advertisement

Reverse-Engineering the Human Auditory Pathway

  • Lloyd Watts
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7311)

Abstract

The goal of reverse-engineering the human brain, starting with the auditory pathway, requires three essential ingredients: Neuroscience knowledge, a sufficiently capable computing platform, and a long-term funding source. By 2003, the neuroscience community had a good understanding of the characterization of sound which is carried out in the cochlea and auditory brainstem, and 1.4 GHz single-core computers with XGA displays were fast enough that it was possible to build computer models capable of running and visualizing these processes in isolation at near biological resolution in real-time, and it was possible to raise venture capital funding to begin the project.  By 2008, these advances had permitted the development of products in the area of two-microphone noise reduction for mobile phones, leading to viable business by 2010, thus establishing a self-sustaining funding source to continue the work into the next decade 2010-2020. By 2011, advances in fMRI, multi-electrode, and behavioral studies have illuminated the cortical brain regions responsible for separating sounds in mixtures, understanding speech in quiet and in noisy environments, producing speech, recognizing speakers, and understanding and responding emotionally to music. 2GHz computers with 8 virtual cores and HD displays now permit models of these advanced auditory brain processes to be simulated and displayed simultaneously in real-time, giving a rich perspective on the concurrent and interacting representations of sound and meaning which are developed and maintained in the brain, and exposing a deeper generality to brain architecture than was evident a decade earlier.  While there is much still to be discovered and implemented in the next decade, we can show demonstrable progress on the scientifically ambitious and commercially important goal of reverse-engineering the human auditory pathway.

Keywords

Speech Recognition Inferior Colliculus Auditory Pathway Primary Auditory Cortex Speaker Identification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Watts, L.: Visualizing Complexity in the Brain. In: Fogel, D., Robinson, C. (eds.) Computational Intelligence: The Experts Speak, pp. 45–56. IEEE Press/Wiley (2003)Google Scholar
  2. 2.
    Watts, L.: Commercializing Audi-tory Neuroscience. In: Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2006 Symposium, pp. 5–14. National Academy of Engineering (2007)Google Scholar
  3. 3.
    Watts, L.: Advanced Noise Reduction for Mobile Telephony. IEEE Computer 41(8), 90–92 (2008)CrossRefGoogle Scholar
  4. 4.
    Watts, L., Massie, D., Sansano, A., Huey, J.: Voice Processors Based on the Human Hearing System. IEEE Micro, 54–61 (March/April 2009)Google Scholar
  5. 5.
    Mesgarani, N., David, S.V., Fritz, J.B., Shamma, S.A.: Influence of context and behavior on stimulus reconstruction from neural activity in primary auditory cortex. J. Neurophysiol. 102(6), 3329–3339 (2009)CrossRefGoogle Scholar
  6. 6.
    Mesgarani, N., Chang, E.: Robust cortical representation of attended speaker in multitalker speech perception. Submitted to Nature (2011)Google Scholar
  7. 7.
    Hickok, G., Poeppel, D.: The cortical organization of speech processing. Nature Reviews Neuroscience 8(5), 393–402 (2007)CrossRefGoogle Scholar
  8. 8.
    von Kriegstein, K., Giraud, A.L.: Distinct functional substrates along the right superior temporal sulcus for the processing of voices. NeuroImage 22, 948–955 (2004)CrossRefGoogle Scholar
  9. 9.
    Peretz, I., Zatorre, R.: Brain organization for music processing. Annual Review of Psychology 56, 89–114 (2005)CrossRefGoogle Scholar
  10. 10.
    Levitin, D.: This is Your Brain on Music. Dutton Adult (2006)Google Scholar
  11. 11.
    Andersen, P., Morris, R., Amaral, D., Bliss, T., O’Keefe, J.: The Hippocampus Book. Oxford University Press (2007)Google Scholar
  12. 12.
    LeDoux, J.: The Emotional Brain. Simon & Schuster (1998)Google Scholar
  13. 13.
    Whalen, P., Phelps, E.: The Human Amygdala. The Guilford Press (2009)Google Scholar
  14. 14.
    Hervais-Adelman, A.: Personal communication (2011)Google Scholar
  15. 15.
    Bregman, A.: Auditory Scene Analysis. MIT Press (1994)Google Scholar
  16. 16.
  17. 17.
    Parsons, C., Young, K., Joensson, M., Brattico, E., Hyam, J., Stein, A., Green, A., Aziz, T., Kringelbach, M.: Ready for action: A role for the brainstem in responding to infant vocalisations. Society For Neurosciences, Poster WW23 299.03 (2011)Google Scholar
  18. 18.
    Hyde, P., Knudsen, E.: Topographic projection from the optic tectum to the auditory space map in the inferior colliculus of the barn owl. J. Comp. Neurol. 421(2), 146–160 (2000)CrossRefGoogle Scholar
  19. 19.
    Calvin, W.: The Cerebral Code. MIT Press (1998)Google Scholar
  20. 20.
    Douglas, R., Martin, K.: In: Shepherd, G. (ed.) The Synaptic Organization of the Brain, 4th edn., pp. 459–510. Oxford University Press (1998)Google Scholar
  21. 21.
    Mountcastle, V.B.: Introduction to the special issue on computation in cortical columns. Cerebral Cortex 13(1), 2–4 (2003)CrossRefGoogle Scholar
  22. 22.
    Dean, T.: A computational model of the cerebral cortex. In: The Proceedings of Twentieth National Conference on Artificial Intelligence (AAAI 2005), pp. 938–943. MIT Press, Cambridge (2005)Google Scholar
  23. 23.
    George, D., Hawkins, J.: A Hierarchical Bayesian Model of Invariant Pattern Recognition in the Visual Cortex. In: Proceedings of the International Joint Conference on Neural Networks (2005)Google Scholar
  24. 24.
    Hawkins, J., Blakeslee, S.: On Intelligence. St. Martin’s Griffin (2005)Google Scholar
  25. 25.
    Hinton, G.E., Osindero, S., Teh, Y.: A fast learning algorithm for deep belief nets. Neural Computation 18, 1527–1554 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Okutomi, M., Kanade, T.: A Multiple-Baseline Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(4), 353–363 (1993)CrossRefGoogle Scholar
  27. 27.
    Dawson, G., Webb, S., Wijsman, E., Schellenberg, G., Estes, A., Munson, J., Faja, S.: Neurocognitive and electrophysiological evidence of altered face processing in parents of children with autism: implications for a model of abnormal development of social brain circuitry in autism. Dev. Psychopathol. 17(3), 679–697 (2005), http://www.ncbi.nlm.nih.gov/pubmed?term=%22Dawson%20G%22%5BAuthor%5DCrossRefGoogle Scholar
  28. 28.
    Dubois, J., Benders, M., Cachia, A., Lazeyras, F., Leuchter, R., Sizonenko, S., Borradori-Tolsa, C., Mangin, J., Hu, P.S.: Mapping the Early Cortical Folding Process in the Preterm Newborn Brain. Cerebral Cortex 18, 1444–1454 (2008)CrossRefGoogle Scholar
  29. 29.
    Kanwisher, N.: Functional specificity in the human brain: A window into the functional architecture of the mind. Proc. Natl. Acad. Sci. USA (2010)Google Scholar
  30. 30.
    Lai, C., Fisher, S., Hurst, J., Vargha-Khadem, F., Monaco, A.: A forkhead-domain gene is mutated in a severe speech and language disorder. Nature 413(6855), 519–523 (2001)CrossRefGoogle Scholar
  31. 31.
    MacDermot, K., Bonora, E., Sykes, N., Coupe, A., Lai, C., Vernes, S., Vargha-Khadem, F., McKenzie, F., Smith, R., Monaco, A., Fisher, S.: Identification of FOXP2 truncation as a novel cause of developmental speech and language deficits. Am. J. Hum. Genet. 76(6), 1074–1080 (2005)CrossRefGoogle Scholar
  32. 32.
  33. 33.
    Konopka, G., Bomar, J., Winden, K., Coppola, G., Jonsson, Z., Gao, F., Peng, S., Preuss, T., Wohlschlegel, J., Geschwind, D.: Human-specific transcriptional regulation of CNS devel-opment genes by FOXP2. Nature 462, 213–217 (2009)CrossRefGoogle Scholar
  34. 34.
  35. 35.
    Vargha-Khadem, et al.: Praxic and nonverbal cognitive deficits in a large family with a genetically transmitted speech and language disorder. Proc. Nat. Acad. Sci. USA 92, 930–933 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lloyd Watts
    • 1
  1. 1.Audience, Inc.Mountain ViewUSA

Personalised recommendations