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Speech Coding in the Brain: Representation of Vowel Formants by Midbrain Neurons Tuned to Sound Fluctuations(1,2,3).

Carney LH, Li T, McDonough JM - eNeuro (2015)

Bottom Line: Additionally, a successful neural code must function for speech in background noise at levels that are tolerated by listeners.The model presented here resolves these problems, and incorporates several key response properties of the nonlinear auditory periphery, including saturation, synchrony capture, and phase locking to both fine structure and envelope temporal features.The hypothesized code is supported by electrophysiological recordings from the inferior colliculus of awake rabbits.

View Article: PubMed Central - HTML - PubMed

Affiliation: Departments of Biomedical Engineering, and Neurobiology & Anatomy, University of Rochester , Rochester, New York 14642.

ABSTRACT
Current models for neural coding of vowels are typically based on linear descriptions of the auditory periphery, and fail at high sound levels and in background noise. These models rely on either auditory nerve discharge rates or phase locking to temporal fine structure. However, both discharge rates and phase locking saturate at moderate to high sound levels, and phase locking is degraded in the CNS at middle to high frequencies. The fact that speech intelligibility is robust over a wide range of sound levels is problematic for codes that deteriorate as the sound level increases. Additionally, a successful neural code must function for speech in background noise at levels that are tolerated by listeners. The model presented here resolves these problems, and incorporates several key response properties of the nonlinear auditory periphery, including saturation, synchrony capture, and phase locking to both fine structure and envelope temporal features. The model also includes the properties of the auditory midbrain, where discharge rates are tuned to amplitude fluctuation rates. The nonlinear peripheral response features create contrasts in the amplitudes of low-frequency neural rate fluctuations across the population. These patterns of fluctuations result in a response profile in the midbrain that encodes vowel formants over a wide range of levels and in background noise. The hypothesized code is supported by electrophysiological recordings from the inferior colliculus of awake rabbits. This model provides information for understanding the structure of cross-linguistic vowel spaces, and suggests strategies for automatic formant detection and speech enhancement for listeners with hearing loss.

No MeSH data available.


Related in: MedlinePlus

A–C, Three bandpass MTFs (blue, as in Fig. 2) with mid-frequency (A), high-frequency (B), and low-frequency (C) BMFs. MTFs for three model cells (red, as in Fig. 2) that are inhibited by the bandpass cells explain three other MTF types in the IC: the more common band-reject (A) and low-pass (B) MTFs, as well as the less common high-pass MTF (C). Model parameters are in Table 1.
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Figure 3: A–C, Three bandpass MTFs (blue, as in Fig. 2) with mid-frequency (A), high-frequency (B), and low-frequency (C) BMFs. MTFs for three model cells (red, as in Fig. 2) that are inhibited by the bandpass cells explain three other MTF types in the IC: the more common band-reject (A) and low-pass (B) MTFs, as well as the less common high-pass MTF (C). Model parameters are in Table 1.

Mentions: Model parameters


Speech Coding in the Brain: Representation of Vowel Formants by Midbrain Neurons Tuned to Sound Fluctuations(1,2,3).

Carney LH, Li T, McDonough JM - eNeuro (2015)

A–C, Three bandpass MTFs (blue, as in Fig. 2) with mid-frequency (A), high-frequency (B), and low-frequency (C) BMFs. MTFs for three model cells (red, as in Fig. 2) that are inhibited by the bandpass cells explain three other MTF types in the IC: the more common band-reject (A) and low-pass (B) MTFs, as well as the less common high-pass MTF (C). Model parameters are in Table 1.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4596011&req=5

Figure 3: A–C, Three bandpass MTFs (blue, as in Fig. 2) with mid-frequency (A), high-frequency (B), and low-frequency (C) BMFs. MTFs for three model cells (red, as in Fig. 2) that are inhibited by the bandpass cells explain three other MTF types in the IC: the more common band-reject (A) and low-pass (B) MTFs, as well as the less common high-pass MTF (C). Model parameters are in Table 1.
Mentions: Model parameters

Bottom Line: Additionally, a successful neural code must function for speech in background noise at levels that are tolerated by listeners.The model presented here resolves these problems, and incorporates several key response properties of the nonlinear auditory periphery, including saturation, synchrony capture, and phase locking to both fine structure and envelope temporal features.The hypothesized code is supported by electrophysiological recordings from the inferior colliculus of awake rabbits.

View Article: PubMed Central - HTML - PubMed

Affiliation: Departments of Biomedical Engineering, and Neurobiology & Anatomy, University of Rochester , Rochester, New York 14642.

ABSTRACT
Current models for neural coding of vowels are typically based on linear descriptions of the auditory periphery, and fail at high sound levels and in background noise. These models rely on either auditory nerve discharge rates or phase locking to temporal fine structure. However, both discharge rates and phase locking saturate at moderate to high sound levels, and phase locking is degraded in the CNS at middle to high frequencies. The fact that speech intelligibility is robust over a wide range of sound levels is problematic for codes that deteriorate as the sound level increases. Additionally, a successful neural code must function for speech in background noise at levels that are tolerated by listeners. The model presented here resolves these problems, and incorporates several key response properties of the nonlinear auditory periphery, including saturation, synchrony capture, and phase locking to both fine structure and envelope temporal features. The model also includes the properties of the auditory midbrain, where discharge rates are tuned to amplitude fluctuation rates. The nonlinear peripheral response features create contrasts in the amplitudes of low-frequency neural rate fluctuations across the population. These patterns of fluctuations result in a response profile in the midbrain that encodes vowel formants over a wide range of levels and in background noise. The hypothesized code is supported by electrophysiological recordings from the inferior colliculus of awake rabbits. This model provides information for understanding the structure of cross-linguistic vowel spaces, and suggests strategies for automatic formant detection and speech enhancement for listeners with hearing loss.

No MeSH data available.


Related in: MedlinePlus