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Efficient auditory coding

Author

Listed:
  • Evan C. Smith

    (Department of Psychology
    Carnegie Mellon University)

  • Michael S. Lewicki

    (Carnegie Mellon University
    Carnegie Mellon University)

Abstract

The auditory neural code must serve a wide range of auditory tasks that require great sensitivity in time and frequency and be effective over the diverse array of sounds present in natural acoustic environments. It has been suggested1,2,3,4,5 that sensory systems might have evolved highly efficient coding strategies to maximize the information conveyed to the brain while minimizing the required energy and neural resources. Here we show that, for natural sounds, the complete acoustic waveform can be represented efficiently with a nonlinear model based on a population spike code. In this model, idealized spikes encode the precise temporal positions and magnitudes of underlying acoustic features. We find that when the features are optimized for coding either natural sounds or speech, they show striking similarities to time-domain cochlear filter estimates, have a frequency-bandwidth dependence similar to that of auditory nerve fibres, and yield significantly greater coding efficiency than conventional signal representations. These results indicate that the auditory code might approach an information theoretic optimum and that the acoustic structure of speech might be adapted to the coding capacity of the mammalian auditory system.

Suggested Citation

  • Evan C. Smith & Michael S. Lewicki, 2006. "Efficient auditory coding," Nature, Nature, vol. 439(7079), pages 978-982, February.
    • Handle: RePEc:nat:nature:v:439:y:2006:i:7079:d:10.1038_nature04485
    DOI: 10.1038/nature04485
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    Citations

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    Cited by:

    1. Sam V Norman-Haignere & Josh H McDermott, 2018. "Neural responses to natural and model-matched stimuli reveal distinct computations in primary and nonprimary auditory cortex," PLOS Biology, Public Library of Science, vol. 16(12), pages 1-46, December.
    2. Lingyun Zhao & Li Zhaoping, 2011. "Understanding Auditory Spectro-Temporal Receptive Fields and Their Changes with Input Statistics by Efficient Coding Principles," PLOS Computational Biology, Public Library of Science, vol. 7(8), pages 1-16, August.
    3. Gonzalo H Otazu & Christian Leibold, 2011. "A Corticothalamic Circuit Model for Sound Identification in Complex Scenes," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-15, September.
    4. Oded Barzelay & Miriam Furst & Omri Barak, 2017. "A New Approach to Model Pitch Perception Using Sparse Coding," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-36, January.
    5. Noga Mosheiff & Haggai Agmon & Avraham Moriel & Yoram Burak, 2017. "An efficient coding theory for a dynamic trajectory predicts non-uniform allocation of entorhinal grid cells to modules," PLOS Computational Biology, Public Library of Science, vol. 13(6), pages 1-19, June.
    6. Jonathan Schaffner & Sherry Dongqi Bao & Philippe N. Tobler & Todd A. Hare & Rafael Polania, 2023. "Sensory perception relies on fitness-maximizing codes," Nature Human Behaviour, Nature, vol. 7(7), pages 1135-1151, July.
    7. Jonathan J Hunt & Peter Dayan & Geoffrey J Goodhill, 2013. "Sparse Coding Can Predict Primary Visual Cortex Receptive Field Changes Induced by Abnormal Visual Input," PLOS Computational Biology, Public Library of Science, vol. 9(5), pages 1-17, May.
    8. Joseph D. Zak & Gautam Reddy & Vaibhav Konanur & Venkatesh N. Murthy, 2024. "Distinct information conveyed to the olfactory bulb by feedforward input from the nose and feedback from the cortex," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    9. Tomas Barta & Lubomir Kostal, 2019. "The effect of inhibition on rate code efficiency indicators," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-21, December.
    10. Jacob N Oppenheim & Pavel Isakov & Marcelo O Magnasco, 2013. "Degraded Time-Frequency Acuity to Time-Reversed Notes," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-6, June.
    11. Lubomir Kostal & Petr Lansky & Jean-Pierre Rospars, 2008. "Efficient Olfactory Coding in the Pheromone Receptor Neuron of a Moth," PLOS Computational Biology, Public Library of Science, vol. 4(4), pages 1-11, April.

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