Spectro-Temporal Processing in a Two-Stream Computational Model of Auditory Cortex

Neural processing of sounds in the dorsal and ventral streams of the (human) auditory cortex is optimized for analyzing fine-grained temporal and spectral information, respectively. Here we use a Wilson and Cowan firing-rate modeling framework to simulate spectro-temporal processing of sounds in the...

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Main Authors: Isma Zulfiqar, Michelle Moerel, Elia Formisano
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-01-01
Series:Frontiers in Computational Neuroscience
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/fncom.2019.00095/full
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spelling doaj-443f01639efd4de09b93ac5ef9f7a00b2020-11-25T01:11:51ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882020-01-011310.3389/fncom.2019.00095498552Spectro-Temporal Processing in a Two-Stream Computational Model of Auditory CortexIsma Zulfiqar0Michelle Moerel1Michelle Moerel2Michelle Moerel3Elia Formisano4Elia Formisano5Elia Formisano6Maastricht Centre for Systems Biology, Maastricht University, Maastricht, NetherlandsMaastricht Centre for Systems Biology, Maastricht University, Maastricht, NetherlandsDepartment of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, NetherlandsMaastricht Brain Imaging Center, Maastricht, NetherlandsMaastricht Centre for Systems Biology, Maastricht University, Maastricht, NetherlandsDepartment of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, NetherlandsMaastricht Brain Imaging Center, Maastricht, NetherlandsNeural processing of sounds in the dorsal and ventral streams of the (human) auditory cortex is optimized for analyzing fine-grained temporal and spectral information, respectively. Here we use a Wilson and Cowan firing-rate modeling framework to simulate spectro-temporal processing of sounds in these auditory streams and to investigate the link between neural population activity and behavioral results of psychoacoustic experiments. The proposed model consisted of two core (A1 and R, representing primary areas) and two belt (Slow and Fast, representing rostral and caudal processing respectively) areas, differing in terms of their spectral and temporal response properties. First, we simulated the responses to amplitude modulated (AM) noise and tones. In agreement with electrophysiological results, we observed an area-dependent transition from a temporal (synchronization) to a rate code when moving from low to high modulation rates. Simulated neural responses in a task of amplitude modulation detection suggested that thresholds derived from population responses in core areas closely resembled those of psychoacoustic experiments in human listeners. For tones, simulated modulation threshold functions were found to be dependent on the carrier frequency. Second, we simulated the responses to complex tones with missing fundamental stimuli and found that synchronization of responses in the Fast area accurately encoded pitch, with the strength of synchronization depending on number and order of harmonic components. Finally, using speech stimuli, we showed that the spectral and temporal structure of the speech was reflected in parallel by the modeled areas. The analyses highlighted that the Slow stream coded with high spectral precision the aspects of the speech signal characterized by slow temporal changes (e.g., prosody), while the Fast stream encoded primarily the faster changes (e.g., phonemes, consonants, temporal pitch). Interestingly, the pitch of a speaker was encoded both spatially (i.e., tonotopically) in Slow area and temporally in Fast area. Overall, performed simulations showed that the model is valuable for generating hypotheses on how the different cortical areas/streams may contribute toward behaviorally relevant aspects of auditory processing. The model can be used in combination with physiological models of neurovascular coupling to generate predictions for human functional MRI experiments.https://www.frontiersin.org/article/10.3389/fncom.2019.00095/fullauditory cortexsound processingdynamic neuronal modelingtemporal codingrate coding
collection DOAJ
language English
format Article
sources DOAJ
author Isma Zulfiqar
Michelle Moerel
Michelle Moerel
Michelle Moerel
Elia Formisano
Elia Formisano
Elia Formisano
spellingShingle Isma Zulfiqar
Michelle Moerel
Michelle Moerel
Michelle Moerel
Elia Formisano
Elia Formisano
Elia Formisano
Spectro-Temporal Processing in a Two-Stream Computational Model of Auditory Cortex
Frontiers in Computational Neuroscience
auditory cortex
sound processing
dynamic neuronal modeling
temporal coding
rate coding
author_facet Isma Zulfiqar
Michelle Moerel
Michelle Moerel
Michelle Moerel
Elia Formisano
Elia Formisano
Elia Formisano
author_sort Isma Zulfiqar
title Spectro-Temporal Processing in a Two-Stream Computational Model of Auditory Cortex
title_short Spectro-Temporal Processing in a Two-Stream Computational Model of Auditory Cortex
title_full Spectro-Temporal Processing in a Two-Stream Computational Model of Auditory Cortex
title_fullStr Spectro-Temporal Processing in a Two-Stream Computational Model of Auditory Cortex
title_full_unstemmed Spectro-Temporal Processing in a Two-Stream Computational Model of Auditory Cortex
title_sort spectro-temporal processing in a two-stream computational model of auditory cortex
publisher Frontiers Media S.A.
series Frontiers in Computational Neuroscience
issn 1662-5188
publishDate 2020-01-01
description Neural processing of sounds in the dorsal and ventral streams of the (human) auditory cortex is optimized for analyzing fine-grained temporal and spectral information, respectively. Here we use a Wilson and Cowan firing-rate modeling framework to simulate spectro-temporal processing of sounds in these auditory streams and to investigate the link between neural population activity and behavioral results of psychoacoustic experiments. The proposed model consisted of two core (A1 and R, representing primary areas) and two belt (Slow and Fast, representing rostral and caudal processing respectively) areas, differing in terms of their spectral and temporal response properties. First, we simulated the responses to amplitude modulated (AM) noise and tones. In agreement with electrophysiological results, we observed an area-dependent transition from a temporal (synchronization) to a rate code when moving from low to high modulation rates. Simulated neural responses in a task of amplitude modulation detection suggested that thresholds derived from population responses in core areas closely resembled those of psychoacoustic experiments in human listeners. For tones, simulated modulation threshold functions were found to be dependent on the carrier frequency. Second, we simulated the responses to complex tones with missing fundamental stimuli and found that synchronization of responses in the Fast area accurately encoded pitch, with the strength of synchronization depending on number and order of harmonic components. Finally, using speech stimuli, we showed that the spectral and temporal structure of the speech was reflected in parallel by the modeled areas. The analyses highlighted that the Slow stream coded with high spectral precision the aspects of the speech signal characterized by slow temporal changes (e.g., prosody), while the Fast stream encoded primarily the faster changes (e.g., phonemes, consonants, temporal pitch). Interestingly, the pitch of a speaker was encoded both spatially (i.e., tonotopically) in Slow area and temporally in Fast area. Overall, performed simulations showed that the model is valuable for generating hypotheses on how the different cortical areas/streams may contribute toward behaviorally relevant aspects of auditory processing. The model can be used in combination with physiological models of neurovascular coupling to generate predictions for human functional MRI experiments.
topic auditory cortex
sound processing
dynamic neuronal modeling
temporal coding
rate coding
url https://www.frontiersin.org/article/10.3389/fncom.2019.00095/full
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