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Efficient temporal processing of naturalistic sounds.

Lesica NA, Grothe B - PLoS ONE (2008)

Bottom Line: We find that the onset of ambient noise evokes a change in receptive field dynamics that corresponds to a change from bandpass to lowpass temporal filtering.We show that these changes occur within a few hundred milliseconds of the onset of the noise and are evident across a range of overall stimulus intensities.Using a simple model, we illustrate how these changes in temporal processing exploit differences in the statistical properties of vocalizations and ambient noises to increase the information in the neural response in a manner consistent with the principles of efficient coding.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology II, Ludwig-Maximilians-University Munich, Martinsried, Germany. lesica@zi.biologie.uni-muenchen.de

ABSTRACT
In this study, we investigate the ability of the mammalian auditory pathway to adapt its strategy for temporal processing under natural stimulus conditions. We derive temporal receptive fields from the responses of neurons in the inferior colliculus to vocalization stimuli with and without additional ambient noise. We find that the onset of ambient noise evokes a change in receptive field dynamics that corresponds to a change from bandpass to lowpass temporal filtering. We show that these changes occur within a few hundred milliseconds of the onset of the noise and are evident across a range of overall stimulus intensities. Using a simple model, we illustrate how these changes in temporal processing exploit differences in the statistical properties of vocalizations and ambient noises to increase the information in the neural response in a manner consistent with the principles of efficient coding.

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Temporal properties of vocalizations and ambient noises.a,b) Spectrograms of example sounds from the vocalization (Mexican hairy porcupine, human, and common nightingale) and ambient noise ensembles (wind, vacuum cleaner, rain), with the AMs in nightingale song and rain in the frequency band around 6.5 KHz shown below. c,d) The amplitude distributions of the AMs in the sounds shown in a and b in the carrier frequency band around 6.5 KHz. Colors correspond to the boxes in the upper righthand corner of the spectrograms in a and b. AMs were normalized to have a minimum amplitude of 0 and a maximum amplitude of 1. e,f) The power spectra of the AMs in the sounds shown in a and b in the carrier frequency band around 6.5 KHz, with the gray band denoting the 20–120 Hz frequency range. AMs were normalized as in c and d. g) The parameter values that provided the optimal fits for the average amplitude distribution of the AMs in each sound ensemble for a range of carrier frequencies (exponential distribution for vocalizations, Rayleigh distribution for ambient noises). Each sound ensemble was divided into 10 segments, and the error bars in panel g denote one standard deviation of the distribution of optimal parameter values across these segments. h) The value of α that provided the best fit of the function 1/fα to the average power spectra of the AMs in each sound ensemble in the 20–120 Hz frequency range for a range of carrier frequencies.
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pone-0001655-g001: Temporal properties of vocalizations and ambient noises.a,b) Spectrograms of example sounds from the vocalization (Mexican hairy porcupine, human, and common nightingale) and ambient noise ensembles (wind, vacuum cleaner, rain), with the AMs in nightingale song and rain in the frequency band around 6.5 KHz shown below. c,d) The amplitude distributions of the AMs in the sounds shown in a and b in the carrier frequency band around 6.5 KHz. Colors correspond to the boxes in the upper righthand corner of the spectrograms in a and b. AMs were normalized to have a minimum amplitude of 0 and a maximum amplitude of 1. e,f) The power spectra of the AMs in the sounds shown in a and b in the carrier frequency band around 6.5 KHz, with the gray band denoting the 20–120 Hz frequency range. AMs were normalized as in c and d. g) The parameter values that provided the optimal fits for the average amplitude distribution of the AMs in each sound ensemble for a range of carrier frequencies (exponential distribution for vocalizations, Rayleigh distribution for ambient noises). Each sound ensemble was divided into 10 segments, and the error bars in panel g denote one standard deviation of the distribution of optimal parameter values across these segments. h) The value of α that provided the best fit of the function 1/fα to the average power spectra of the AMs in each sound ensemble in the 20–120 Hz frequency range for a range of carrier frequencies.

Mentions: Before testing the ability of the auditory system to adapt its strategy for the temporal processing of vocalizations in the presence of ambient noise, we first investigated the statistical properties of these two classes of sounds. We compiled two ensembles of sounds containing representative examples of vocalizations (animal calls, human speech, bird songs) and sustained ambient noises (wind, vacuum cleaner, etc.). The spectrograms of several example sounds are shown in figures 1a and b.


Efficient temporal processing of naturalistic sounds.

Lesica NA, Grothe B - PLoS ONE (2008)

Temporal properties of vocalizations and ambient noises.a,b) Spectrograms of example sounds from the vocalization (Mexican hairy porcupine, human, and common nightingale) and ambient noise ensembles (wind, vacuum cleaner, rain), with the AMs in nightingale song and rain in the frequency band around 6.5 KHz shown below. c,d) The amplitude distributions of the AMs in the sounds shown in a and b in the carrier frequency band around 6.5 KHz. Colors correspond to the boxes in the upper righthand corner of the spectrograms in a and b. AMs were normalized to have a minimum amplitude of 0 and a maximum amplitude of 1. e,f) The power spectra of the AMs in the sounds shown in a and b in the carrier frequency band around 6.5 KHz, with the gray band denoting the 20–120 Hz frequency range. AMs were normalized as in c and d. g) The parameter values that provided the optimal fits for the average amplitude distribution of the AMs in each sound ensemble for a range of carrier frequencies (exponential distribution for vocalizations, Rayleigh distribution for ambient noises). Each sound ensemble was divided into 10 segments, and the error bars in panel g denote one standard deviation of the distribution of optimal parameter values across these segments. h) The value of α that provided the best fit of the function 1/fα to the average power spectra of the AMs in each sound ensemble in the 20–120 Hz frequency range for a range of carrier frequencies.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2249929&req=5

pone-0001655-g001: Temporal properties of vocalizations and ambient noises.a,b) Spectrograms of example sounds from the vocalization (Mexican hairy porcupine, human, and common nightingale) and ambient noise ensembles (wind, vacuum cleaner, rain), with the AMs in nightingale song and rain in the frequency band around 6.5 KHz shown below. c,d) The amplitude distributions of the AMs in the sounds shown in a and b in the carrier frequency band around 6.5 KHz. Colors correspond to the boxes in the upper righthand corner of the spectrograms in a and b. AMs were normalized to have a minimum amplitude of 0 and a maximum amplitude of 1. e,f) The power spectra of the AMs in the sounds shown in a and b in the carrier frequency band around 6.5 KHz, with the gray band denoting the 20–120 Hz frequency range. AMs were normalized as in c and d. g) The parameter values that provided the optimal fits for the average amplitude distribution of the AMs in each sound ensemble for a range of carrier frequencies (exponential distribution for vocalizations, Rayleigh distribution for ambient noises). Each sound ensemble was divided into 10 segments, and the error bars in panel g denote one standard deviation of the distribution of optimal parameter values across these segments. h) The value of α that provided the best fit of the function 1/fα to the average power spectra of the AMs in each sound ensemble in the 20–120 Hz frequency range for a range of carrier frequencies.
Mentions: Before testing the ability of the auditory system to adapt its strategy for the temporal processing of vocalizations in the presence of ambient noise, we first investigated the statistical properties of these two classes of sounds. We compiled two ensembles of sounds containing representative examples of vocalizations (animal calls, human speech, bird songs) and sustained ambient noises (wind, vacuum cleaner, etc.). The spectrograms of several example sounds are shown in figures 1a and b.

Bottom Line: We find that the onset of ambient noise evokes a change in receptive field dynamics that corresponds to a change from bandpass to lowpass temporal filtering.We show that these changes occur within a few hundred milliseconds of the onset of the noise and are evident across a range of overall stimulus intensities.Using a simple model, we illustrate how these changes in temporal processing exploit differences in the statistical properties of vocalizations and ambient noises to increase the information in the neural response in a manner consistent with the principles of efficient coding.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology II, Ludwig-Maximilians-University Munich, Martinsried, Germany. lesica@zi.biologie.uni-muenchen.de

ABSTRACT
In this study, we investigate the ability of the mammalian auditory pathway to adapt its strategy for temporal processing under natural stimulus conditions. We derive temporal receptive fields from the responses of neurons in the inferior colliculus to vocalization stimuli with and without additional ambient noise. We find that the onset of ambient noise evokes a change in receptive field dynamics that corresponds to a change from bandpass to lowpass temporal filtering. We show that these changes occur within a few hundred milliseconds of the onset of the noise and are evident across a range of overall stimulus intensities. Using a simple model, we illustrate how these changes in temporal processing exploit differences in the statistical properties of vocalizations and ambient noises to increase the information in the neural response in a manner consistent with the principles of efficient coding.

Show MeSH
Related in: MedlinePlus