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%2F68407700%3A21730%2F20%3A00332494" target="_blank" >RIV/68407700:21730/20:00332494 - isvavai.cz</a>
Alternative codes found
RIV/68378041:_____/20:00539954
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0303264719300851" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0303264719300851</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 (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 by up to 92%. Results also suggested the involvement of different neural mechanisms in generating the early and late responses to amplitude transients in the broadband 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
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10602 - Biology (theoretical, mathematical, thermal, cryobiology, biological rhythm), Evolutionary biology
Result continuities
Project
<a href="/en/project/GC16-09086J" target="_blank" >GC16-09086J: Processing of complex sounds in the central auditory system under normal and pathological conditions</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
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
1872-8324
Volume of the periodical
187
Issue of the periodical within the volume
January
Country of publishing house
GB - UNITED KINGDOM
Number of pages
8
Pages from-to
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UT code for WoS article
000508746500007
EID of the result in the Scopus database
2-s2.0-85073003734