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Neuronal populations and single cells representing learned auditory objects

Author

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  • Timothy Q. Gentner

    (University of Chicago)

  • Daniel Margoliash

    (University of Chicago)

Abstract

The neural representations associated with learned auditory behaviours, such as recognizing individuals based on their vocalizations, are not well described. Higher vertebrates learn to recognize complex conspecific vocalizations that comprise sequences of easily identified, naturally occurring auditory objects1,2, which should facilitate the analysis of higher auditory pathways. Here we describe the first example of neurons selective for learned conspecific vocalizations in adult animals—in starlings that have been trained operantly to recognize conspecific songs. The neuronal population is found in a non-primary forebrain auditory region, exhibits increased responses to the set of learned songs compared with novel songs, and shows differential responses to categories of learned songs based on recognition training contingencies. Within the population, many cells respond highly selectively to a subset of specific motifs (acoustic objects) present only in the learned songs. Such neuronal selectivity may contribute to song-recognition behaviour, which in starlings is sensitive to motif identity3,4. In this system, both top-down and bottom-up processes may modify the tuning properties of neurons during recognition learning, giving rise to plastic representations of behaviourally meaningful auditory objects.

Suggested Citation

  • Timothy Q. Gentner & Daniel Margoliash, 2003. "Neuronal populations and single cells representing learned auditory objects," Nature, Nature, vol. 424(6949), pages 669-674, August.
  • Handle: RePEc:nat:nature:v:424:y:2003:i:6949:d:10.1038_nature01731
    DOI: 10.1038/nature01731
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    Cited by:

    1. Margot C Bjoring & C Daniel Meliza, 2019. "A low-threshold potassium current enhances sparseness and reliability in a model of avian auditory cortex," PLOS Computational Biology, Public Library of Science, vol. 15(1), pages 1-20, January.
    2. V Anne Smith & Jing Yu & Tom V Smulders & Alexander J Hartemink & Erich D Jarvis, 2006. "Computational Inference of Neural Information Flow Networks," PLOS Computational Biology, Public Library of Science, vol. 2(11), pages 1-14, November.

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