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Efficient auditory coding

Nature volume 439, pages 978–982 (2006)Cite this article

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.

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Figure 1: Representing a natural sound with the use of spikes. A brief segment of the word ‘canteen’ (input) is represented as a spike code (top).
Figure 2: Efficient codes for natural sounds predict revcor filter shapes and population characteristics.
Figure 3: Human speech is adapted to the mammalian cochlear code.
Figure 4: Fidelity–rate curves for Fourier, wavelet and spike codes.

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Acknowledgements

E.C.S. was supported by a National Institutes of Health training grant. This material is based on work supported by a National Science Foundation grant to M.S.L. Empirical data in this paper were acquired from Boston University's Earlab, an online, freely accessible auditory database (http://earlab.bu.edu). Author Contributions M.S.L. and E.C.S. developed the model, analysed the results and wrote the paper together; E.C.S. designed and ran the simulations.

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Authors and Affiliations

  1. Department of Psychology,

    Evan C. Smith

  2. Center for the Neural Basis of Cognition, Carnegie Mellon University, 4400 Fifth Avenue, Pennsylvania, 15213, Pittsburgh, USA

    Evan C. Smith & Michael S. Lewicki

  3. Department of Computer Science, Carnegie Mellon University, 4400 Fifth Avenue, Pennsylvania, 15213, Pittsburgh, USA

    Michael S. Lewicki

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  1. Evan C. Smith
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  2. Michael S. Lewicki
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Correspondence to Michael S. Lewicki.

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Supplementary information

Supplementary Discussion

This file contains additional discussion on the non-linear nature of the spike coding algorithm and comparison of learned kernels and auditory revcor filters.

Supplementary Methods

This file contains additional details of the methods used in this study.

Supplementary Figure 1

Alternate efficient codes emerge when training on other sound ensembles.

Supplementary Figure 2

A quantitative comparison of the correlation between cat revcor filters and different codes.

Supplementary Figure 3

Characterization of spike code kernel functions by reverse correlation.

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Smith, E., Lewicki, M. Efficient auditory coding. Nature 439, 978–982 (2006). https://doi.org/10.1038/nature04485

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