Responses of midbrain auditory neurons to two different environmental sounds—A new approach on cross-sound modeling

T. R. Chang, D. Šuta, T. W. Chiu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

When modeling auditory responses to environmental sounds, results are satisfactory if both training and testing are restricted to datasets of one type of sound. To predict 'cross-sound' responses (i.e., to predict the response to one type of sound e.g., rat Eating sound, after training with another type of sound e.g., rat Drinking sound), performance is typically poor. Here we implemented a novel approach to improve such cross-sound modeling (single unit datasets were collected at the auditory midbrain of anesthetized rats). The method had two key features: (a) population responses (e.g., average of 32 units) instead of responses of individual units were analyzed; and (b) the long sound segment was first divided into short segments (single sound-bouts), their similarity was then computed over a new metric involving the response (called Stimulus Response Model map or SRM map), and finally similar sound-bouts (regardless of sound type) and their associated responses (peri-stimulus time histograms, PSTHs) were modelled. Specifically, a committee machine model (artificial neural networks with 20 stratified spectral inputs) was trained with datasets from one sound type before predicting PSTH responses to another sound type. Model performance was markedly improved up to 92%. Results also suggested the involvement of different neural mechanisms in generating the early and late responses to amplitude transients in the broad-band environmental sounds. We concluded that it is possible to perform rather satisfactory cross-sound modeling on datasets grouped together based on their similarities in terms of the new metric of SRM map.

Original languageEnglish
Article number104021
JournalBioSystems
Volume187
DOIs
StatePublished - Jan 2020

Keywords

  • Artificial neural network
  • Complex sound processing
  • Cross-sound modeling
  • Inferior colliculus
  • Rat

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