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A Model for Periodicity Coding in the Auditory System

  • Socrates Deligeorges
  • David C. Mountain

Abstract

The processing of complex sounds is thought to require the analysis of both spectral and temporal features. The initial processing of temporal features is believed to take place in a monaural pathway or pathways from the cochlea to the inferior colliculus (IC). We hypothesize that temporal processing begins with enhancement of temporal features by the cochlea and by cells in the cochlear nucleus and ends with a coincidence detection mechanism in the IC.

Amplitude modulated (AM) stimuli were used in all model simulations for comparison with data from physiological experiments. The peripheral model consists of a basilar membrane, inner-hair cell, and auditory nerve fiber models. The cochlear model output, was compared to auditory nerve experimental data taken by Joris and Yin (1992) with respect to modulation gain. The cochlear nucleus model, likewise, was compared to data collected by Rhode and Greenberg (1994). The IC model was compared to the findings of Langner and Schreiner (1988).

The result from each stage of the model showed good agreement with the physiological data. At the cochlear level, the model was able to reproduce physiological responses to AM over the same stimulus intensity range as well as frequency range. The cochlear nucleus model also performed well duplicating levels of modulation gain and temporal enhancement seen in the physiological experiments. The IC model was able to process the temporal features passed through the first two model stages and produced modulation transfer functions (MTFs) similar to those seen by Langner and Schreiner. The complete model output can be thought of as a cellular matrix whose activity maps the temporal content of spectral components within an acoustic stimulus.

Keywords

Hair Cell Auditory Nerve Inferior Colliculus Basilar Membrane Cochlear Nucleus 
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.

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Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • Socrates Deligeorges
    • 1
  • David C. Mountain
    • 1
  1. 1.Department Biomedical EngineeringBoston UniversityBostonUSA

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