Goal-driven, neurobiological-inspired convolutional neural network models of human spatial hearing

NEUROCOMPUTING

卷:470

Article

van der Heijden, Kiki^1,2,3  Mehrkanoon, Siamak⁴ 

[1] Radboud Univ Nijmegen, Donders Inst Brain Cognit & Behav, Nijmegen, Netherlands

[2] Columbia Univ, Zuckerman Mind Brain Behav Inst, New York, NY 10027 USA

[3] Maastricht Univ, Maastricht Ctr Syst Biol, Maastricht, Netherlands

[4] Maastricht Univ, Dept Knowledge Engn, Maastricht, Netherlands

关键词: Convolutional neural network;    Human sound localization;    Binaural integration;    Deep learning;   

DOI  :  10.1016/j.neucom.2021.05.104

来源: Elsevier

【 摘 要 】

The human brain effortlessly solves the complex computational task of sound localization using a mixture of spatial cues. How the brain performs this task in naturalistic listening environments (e.g. with reverberation) is not well understood. In the present paper, we build on the success of deep neural net-works at solving complex and high-dimensional problems [1] to develop goal-driven, neurobiological-inspired convolutional neural network (CNN) models of human spatial hearing. After training, we visual-ize and quantify feature representations in intermediate layers to gain insights into the representational mechanisms underlying sound location encoding in CNNs. Our results show that neurobiological-inspired CNN models trained on real-life sounds spatialized with human binaural hearing characteristics can accu-rately predict sound location in the horizontal plane. CNN localization acuity across the azimuth resem-bles human sound localization acuity, but CNN models outperform human sound localization in the back. Training models with different objective functions -that is, minimizing either Euclidean or angular dis-tance -modulates localization acuity in particular ways. Moreover, different implementations of binaural integration result in unique patterns of localization errors that resemble behavioral observations in humans. Finally, feature representations reveal a gradient of spatial selectivity across network layers, starting with broad spatial representations in early layers and progressing to sparse, highly selective spa-tial representations in deeper layers. In sum, our results show that neurobiological-inspired CNNs are a valid approach to modeling human spatial hearing. This work paves the way for future studies combining neural network models with empirical measurements of neural activity to unravel the complex compu-tational mechanisms underlying neural sound location encoding in the human auditory pathway. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

【 授权许可】

Free