Perception in Noise: From Human Perceptive Abilities to Neural Correlates and Computational Models

A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Neuroscience".

Deadline for manuscript submissions: 31 August 2026

Special Issue Editor


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Guest Editor
Paris-Saclay Institute of Neuroscience (NeuroPSI, UMR 9197), University Paris-Saclay, 91400 Saclay, France
Interests: auditory perception; neural networks; communication sounds; auditory system; masking noise; coding in noise; robust representation
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Special Issue Information

Dear Colleagues,

Both humans and animals daily face challenging acoustic environments such as intense background noises that impair the detection, discrimination and identification of behaviorally relevant sounds. A full understanding of these remarkable performances requires three major steps. Describing to what extend a behavioral training can improve the perception of communication sounds in noise is a first step in this field of research. A second step aims to delineate the neural networks underlying of robust perception in noise. A third step is to build computational models allowing dissecting precisely the mechanisms of robust perception in noise from the very first synapse within the auditory system up to the higher-order auditory cortical areas.

This Special Issue of Biology entitled “Perception in Noise: From Human Perceptive Abilities to Neural Correlates and Computational Models” will integrate the human and animals literature aiming at understanding the mechanisms operating during perception in noise. This Special Issue aims to collect both reviews and original articles on recent investigations of neural correlates of perception in noise either with human subjects or with animal models. Theoretical contributions and modeling studies are welcome when they are directly related to understanding perception in noise.

Dr. Jean Marc Edeline
Guest Editor

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Keywords

  • auditory perception
  • neural networks
  • communication sounds
  • auditory system
  • masking noise
  • coding in noise
  • robust representation

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