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

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378041%3A_____%2F20%3A00539954" target="_blank" >RIV/68378041:_____/20:00539954 - isvavai.cz</a>

  • Alternative codes found

    RIV/68407700:21730/20:00332494

  • Result on the web

    <a href="https://www.sciencedirect.com/science/article/abs/pii/S0303264719300851?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0303264719300851?via%3Dihub</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.biosystems.2019.104021" target="_blank" >10.1016/j.biosystems.2019.104021</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

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

  • Original language description

    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 (peristimulus 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.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    30103 - Neurosciences (including psychophysiology)

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2020

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Biosystems

  • ISSN

    0303-2647

  • e-ISSN

  • Volume of the periodical

    187

  • Issue of the periodical within the volume

    jan.

  • Country of publishing house

    IE - IRELAND

  • Number of pages

    8

  • Pages from-to

    104021

  • UT code for WoS article

    000508746500007

  • EID of the result in the Scopus database

    2-s2.0-85073003734