Number of the records: 1
Responses of midbrain auditory neurons to two different environmental sounds-A new approach on cross-sound modeling
- 1.
SYSNO ASEP 0539954 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Responses of midbrain auditory neurons to two different environmental sounds-A new approach on cross-sound modeling Author(s) Chang, T.R. (TW)
Šuta, Daniel (UEM-P) RID
Chiu, T. W. (TW)Article number 104021 Source Title Biosystems. - : Elsevier - ISSN 0303-2647
Roč. 187, jan. (2020)Number of pages 8 s. Language eng - English Country IE - Ireland Keywords cross-sound modeling ; complex sound processing ; artificial neural network Subject RIV FH - Neurology OECD category Neurosciences (including psychophysiology R&D Projects GC16-09086J GA ČR - Czech Science Foundation (CSF) Method of publishing Limited access Institutional support UEM-P - RVO:68378041 UT WOS 000508746500007 EID SCOPUS 85073003734 DOI 10.1016/j.biosystems.2019.104021 Annotation 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. Workplace Institute of Experimental Medicine Contact Lenka Koželská, lenka.kozelska@iem.cas.cz, Tel.: 241 062 218, 296 442 218 Year of Publishing 2021 Electronic address https://www.sciencedirect.com/science/article/abs/pii/S0303264719300851?via%3Dihub
Number of the records: 1