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Optimal neural population coding of an auditory spatial cue

Author

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  • Nicol S. Harper

    (University College London
    CoMPLEX, University College London)

  • David McAlpine

    (University College London)

Abstract

A sound, depending on the position of its source, can take more time to reach one ear than the other. This interaural (between the ears) time difference (ITD) provides a major cue for determining the source location1,2. Many auditory neurons are sensitive to ITDs3,4, but the means by which such neurons represent ITD is a contentious issue. Recent studies question whether the classical general model (the Jeffress model5) applies across species6,7. Here we show that ITD coding strategies of different species can be explained by a unifying principle: that the ITDs an animal naturally encounters should be coded with maximal accuracy. Using statistical techniques and a stochastic neural model, we demonstrate that the optimal coding strategy for ITD depends critically on head size and sound frequency. For small head sizes and/or low-frequency sounds, the optimal coding strategy tends towards two distinct sub-populations tuned to ITDs outside the range created by the head. This is consistent with recent observations in small mammals6,7. For large head sizes and/or high frequencies, the optimal strategy is a homogeneous distribution of ITD tunings within the range created by the head. This is consistent with observations in the barn owl8,9,10. For humans, the optimal strategy to code ITDs from an acoustically measured distribution depends on frequency; above 400 Hz a homogeneous distribution is optimal, and below 400 Hz distinct sub-populations are optimal.

Suggested Citation

  • Nicol S. Harper & David McAlpine, 2004. "Optimal neural population coding of an auditory spatial cue," Nature, Nature, vol. 430(7000), pages 682-686, August.
  • Handle: RePEc:nat:nature:v:430:y:2004:i:7000:d:10.1038_nature02768
    DOI: 10.1038/nature02768
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    Cited by:

    1. Marie Levakova & Lubomir Kostal & Christelle Monsempès & Vincent Jacob & Philippe Lucas, 2018. "Moth olfactory receptor neurons adjust their encoding efficiency to temporal statistics of pheromone fluctuations," PLOS Computational Biology, Public Library of Science, vol. 14(11), pages 1-17, November.
    2. Filippo Moro & Emmanuel Hardy & Bruno Fain & Thomas Dalgaty & Paul Clémençon & Alessio Prà & Eduardo Esmanhotto & Niccolò Castellani & François Blard & François Gardien & Thomas Mesquida & François Ru, 2022. "Neuromorphic object localization using resistive memories and ultrasonic transducers," Nature Communications, Nature, vol. 13(1), pages 1-13, December.

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