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Spiking network optimized for word recognition in noise predicts auditory system hierarchy.

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Title: Spiking network optimized for word recognition in noise predicts auditory system hierarchy.
Authors: Khatami F; Department of Bioengineering, School of Engineering and Computer Science, University of the Pacific, Stockton, California, United States of America.; Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut, United States of America., Escabí MA; Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut, United States of America.; Department of Electrical and Computer Engineering, University of Connecticut, Storrs, Connecticut, United States of America.; Department of Psychological Sciences, University of Connecticut, Storrs, Connecticut, United States of America.; Connecticut Institute for Brain and Cognitive Sciences, University of Connecticut, Storrs, Connecticut, United States of America.
Source: PLoS computational biology [PLoS Comput Biol] 2020 Jun 19; Vol. 16 (6), pp. e1007558. Date of Electronic Publication: 2020 Jun 19 (Print Publication: 2020).
Publication Type: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
Language: English
Journal Info: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE
Imprint Name(s): Original Publication: San Francisco, CA : Public Library of Science, [2005]-
MeSH Terms: Auditory Perception* , Language* , Pattern Recognition, Physiological*, Auditory Cortex/*physiology , Auditory Pathways/*physiology, Brain/physiology ; Acoustic Stimulation ; Acoustics ; Brain Mapping ; Computer Simulation ; Humans ; Models, Neurological ; Nerve Net ; Neurons ; Noise ; Signal-To-Noise Ratio ; Sound
Abstract: The auditory neural code is resilient to acoustic variability and capable of recognizing sounds amongst competing sound sources, yet, the transformations enabling noise robust abilities are largely unknown. We report that a hierarchical spiking neural network (HSNN) optimized to maximize word recognition accuracy in noise and multiple talkers predicts organizational hierarchy of the ascending auditory pathway. Comparisons with data from auditory nerve, midbrain, thalamus and cortex reveals that the optimal HSNN predicts several transformations of the ascending auditory pathway including a sequential loss of temporal resolution and synchronization ability, increasing sparseness, and selectivity. The optimal organizational scheme enhances performance by selectively filtering out noise and fast temporal cues such as voicing periodicity, that are not directly relevant to the word recognition task. An identical network arranged to enable high information transfer fails to predict auditory pathway organization and has substantially poorer performance. Furthermore, conventional single-layer linear and nonlinear receptive field networks that capture the overall feature extraction of the HSNN fail to achieve similar performance. The findings suggest that the auditory pathway hierarchy and its sequential nonlinear feature extraction computations enhance relevant cues while removing non-informative sources of noise, thus enhancing the representation of sounds in noise impoverished conditions.
Competing Interests: The authors have declared that no competing interests exist.
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Proc Natl Acad Sci U S A. 1996 Jul 23;93(15):8005-10. (PMID: 8755593)
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Vision Res. 1984;24(6):549-64. (PMID: 6740975)
Grant Information: R01 DC015138 United States DC NIDCD NIH HHS
Entry Date(s): Date Created: 20200620 Date Completed: 20200824 Latest Revision: 20211022
Update Code: 20260130
PubMed Central ID: PMC7329140
DOI: 10.1371/journal.pcbi.1007558
PMID: 32559204
Database: MEDLINE
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  Data: The auditory neural code is resilient to acoustic variability and capable of recognizing sounds amongst competing sound sources, yet, the transformations enabling noise robust abilities are largely unknown. We report that a hierarchical spiking neural network (HSNN) optimized to maximize word recognition accuracy in noise and multiple talkers predicts organizational hierarchy of the ascending auditory pathway. Comparisons with data from auditory nerve, midbrain, thalamus and cortex reveals that the optimal HSNN predicts several transformations of the ascending auditory pathway including a sequential loss of temporal resolution and synchronization ability, increasing sparseness, and selectivity. The optimal organizational scheme enhances performance by selectively filtering out noise and fast temporal cues such as voicing periodicity, that are not directly relevant to the word recognition task. An identical network arranged to enable high information transfer fails to predict auditory pathway organization and has substantially poorer performance. Furthermore, conventional single-layer linear and nonlinear receptive field networks that capture the overall feature extraction of the HSNN fail to achieve similar performance. The findings suggest that the auditory pathway hierarchy and its sequential nonlinear feature extraction computations enhance relevant cues while removing non-informative sources of noise, thus enhancing the representation of sounds in noise impoverished conditions.
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  Data: The authors have declared that no competing interests exist.
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      – TitleFull: Spiking network optimized for word recognition in noise predicts auditory system hierarchy.
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          Identifiers:
            – Type: issn-electronic
              Value: 1553-7358
          Numbering:
            – Type: volume
              Value: 16
            – Type: issue
              Value: 6
          Titles:
            – TitleFull: PLoS computational biology
              Type: main
ResultId 1