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Response Properties of Turtle Auditory Afferent Nerve Fibers: Evidence for a High Order Tuning Mechanism

  • Michael Sneary
  • Edwin R. Lewis
Part of the NATO ASI Series book series (NSSA)

Abstract

An early selective advantage of acoustic senses clearly was remote detection of predators (or other dangers) and prey. Therefore, one would expect evolution to have sculpted acoustic receptors for maximum sensitivity. The ultimate limitation on sensitivity is noise, and acoustic receptors must deal with both internal noise (arising from thermal energy within the ear) and external noise (arising from environmental sources, such as the wind). If the spectrum of a signal is different from that of noise, then the signal can be extracted by means of high-resolution spectral filtering; and if the spatial distribution of a signal source is different from that of the environmental noise, then the signal can be extracted by spatial filtering. Given its parallel processing capability and the presence of two ears, the vertebrate CNS is especially well adapted for spatial filtering. Available evidence indicates that this is achieved in large part by temporal correlation (Knudsen,’ 82). Therefore, for maximum sensitivity we expect acoustic sensors to have evolved with peripheral filters that combine high spectral resolution with high temporal resolution. The best way to achieve this combination of properties is to employ a filter with high-order dynamics that produce a relatively broad pass band with sustained, steep high-frequency rolloff and nearly linear phase-vs-frequency. Acting alone, a filter with second-order dynamics, such as those derivable from a simple resonance, cannot do this; indeed it cannot provide high temporal resolution and high spectral resolution at the same time (Lewis’ 87). Therefore, a second-order resonance, acting alone as the peripheral tuning element, appears to be inappropriate for acoustic sensors.

Keywords

Hair Cell High Spectral Resolution Acoustic Sensor Electrical Resonance Acoustic Receptor 
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

© Plenum Press, New York 1989

Authors and Affiliations

  • Michael Sneary
    • 1
  • Edwin R. Lewis
    • 1
  1. 1.Department of Electrical Engineering and Computer SciencesUniversity of CaliforniaBerkeleyUSA

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