Nonlinear processing of a multicomponent communication signal by combination-sensitive neurons in the anuran inferior colliculus

Norman Lee, Katrina M. Schrode, Mark A. Bee

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Diverse animals communicate using multicomponent signals. How a receiver’s central nervous system integrates multiple signal components remains largely unknown. We investigated how female green treefrogs (Hyla cinerea) integrate the multiple spectral components present in male advertisement calls. Typical calls have a bimodal spectrum consisting of formant-like low-frequency (~0.9 kHz) and high-frequency (~2.7 kHz) components that are transduced by different sensory organs in the inner ear. In behavioral experiments, only bimodal calls reliably elicited phonotaxis in no-choice tests, and they were selectively chosen over unimodal calls in two-alternative choice tests. Single neurons in the inferior colliculus of awake, passively listening subjects were classified as combination-insensitive units (27.9%) or combination-sensitive units (72.1%) based on patterns of relative responses to the same bimodal and unimodal calls. Combination-insensitive units responded similarly to the bimodal call and one or both unimodal calls. In contrast, combination-sensitive units exhibited both linear responses (i.e., linear summation) and, more commonly, nonlinear responses (e.g., facilitation, compressive summation, or suppression) to the spectral combination in the bimodal call. These results are consistent with the hypothesis that nonlinearities play potentially critical roles in spectral integration and in the neural processing of multicomponent communication signals.

Original languageEnglish (US)
Pages (from-to)749-772
Number of pages24
JournalJournal of Comparative Physiology A: Neuroethology, Sensory, Neural, and Behavioral Physiology
Volume203
Issue number9
DOIs
StatePublished - Oct 1 2017

Keywords

  • Auditory midbrain
  • Call recognition
  • Complex signal
  • Neural integration
  • Nonlinear processing

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