Reverse-engineering the human auditory pathway

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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. © 2012 Springer-Verlag.

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Watts, L. (2012). Reverse-engineering the human auditory pathway. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7311 LNCS, pp. 47–59). https://doi.org/10.1007/978-3-642-30687-7_3

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