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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, B, Examples of two IC neurons in awake rabbits with bandpass MTF (BF, 1300 Hz; BMF, 130 Hz; A) and band-reject MTF (BF, 2000 Hz; MTF notch at 150 Hz; B). C, Black, Average rate of the bandpass neuron in response to nine vowels with F0 = 148 Hz (Hillenbrand et al., 1995), 65 dB SPL. Blue, Responses of the bandpass SFIE model; red, LPBR model responses; green, energy at the output of a fourth-order gammatone filter at the BF of the cell. Mean and SD of model responses were matched to neural responses. Lines connect the symbols to emphasize patterns in the responses across this set of vowels. D, Average rate of the band-reject neuron (black) to vowels with F0 = 95 Hz presented at 55 dB SPL, with LPBR model predictions (red), energy (green), and for comparison, the SFIE model response (blue). Model parameters were the same as in Fig. 3B.
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Figure 7: A, B, Examples of two IC neurons in awake rabbits with bandpass MTF (BF, 1300 Hz; BMF, 130 Hz; A) and band-reject MTF (BF, 2000 Hz; MTF notch at 150 Hz; B). C, Black, Average rate of the bandpass neuron in response to nine vowels with F0 = 148 Hz (Hillenbrand et al., 1995), 65 dB SPL. Blue, Responses of the bandpass SFIE model; red, LPBR model responses; green, energy at the output of a fourth-order gammatone filter at the BF of the cell. Mean and SD of model responses were matched to neural responses. Lines connect the symbols to emphasize patterns in the responses across this set of vowels. D, Average rate of the band-reject neuron (black) to vowels with F0 = 95 Hz presented at 55 dB SPL, with LPBR model predictions (red), energy (green), and for comparison, the SFIE model response (blue). Model parameters were the same as in Fig. 3B.

Mentions: Figure 7 illustrates responses of two neurons, one with a BF of 1100 Hz and a bandpass MTF (Fig. 7A), and the other with a BF of 2000 Hz and a band-reject MTF (Fig. 7B). Figure 7, C and D, shows the average discharge rates for these two cells in response to nine English vowels (black line), along with predictions provided by the BP SFIE (Figs. 1, 4, blue line) and LPBR (Figs. 1, 4, red line) models. For comparison, predictions based on the energy through a gammatone filter centered at the BF are also shown (Figs. 1, 4, green line). The Pearson product moment correlation coefficient between actual rates and each of the predictions is also shown.


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, B, Examples of two IC neurons in awake rabbits with bandpass MTF (BF, 1300 Hz; BMF, 130 Hz; A) and band-reject MTF (BF, 2000 Hz; MTF notch at 150 Hz; B). C, Black, Average rate of the bandpass neuron in response to nine vowels with F0 = 148 Hz (Hillenbrand et al., 1995), 65 dB SPL. Blue, Responses of the bandpass SFIE model; red, LPBR model responses; green, energy at the output of a fourth-order gammatone filter at the BF of the cell. Mean and SD of model responses were matched to neural responses. Lines connect the symbols to emphasize patterns in the responses across this set of vowels. D, Average rate of the band-reject neuron (black) to vowels with F0 = 95 Hz presented at 55 dB SPL, with LPBR model predictions (red), energy (green), and for comparison, the SFIE model response (blue). Model parameters were the same as in Fig. 3B.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4596011&req=5

Figure 7: A, B, Examples of two IC neurons in awake rabbits with bandpass MTF (BF, 1300 Hz; BMF, 130 Hz; A) and band-reject MTF (BF, 2000 Hz; MTF notch at 150 Hz; B). C, Black, Average rate of the bandpass neuron in response to nine vowels with F0 = 148 Hz (Hillenbrand et al., 1995), 65 dB SPL. Blue, Responses of the bandpass SFIE model; red, LPBR model responses; green, energy at the output of a fourth-order gammatone filter at the BF of the cell. Mean and SD of model responses were matched to neural responses. Lines connect the symbols to emphasize patterns in the responses across this set of vowels. D, Average rate of the band-reject neuron (black) to vowels with F0 = 95 Hz presented at 55 dB SPL, with LPBR model predictions (red), energy (green), and for comparison, the SFIE model response (blue). Model parameters were the same as in Fig. 3B.
Mentions: Figure 7 illustrates responses of two neurons, one with a BF of 1100 Hz and a bandpass MTF (Fig. 7A), and the other with a BF of 2000 Hz and a band-reject MTF (Fig. 7B). Figure 7, C and D, shows the average discharge rates for these two cells in response to nine English vowels (black line), along with predictions provided by the BP SFIE (Figs. 1, 4, blue line) and LPBR (Figs. 1, 4, red line) models. For comparison, predictions based on the energy through a gammatone filter centered at the BF are also shown (Figs. 1, 4, green line). The Pearson product moment correlation coefficient between actual rates and each of the predictions is also shown.

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