Limits...
A neuronal network model for context-dependence of pitch change perception.

Huang C, Englitz B, Shamma S, Rinzel J - Front Comput Neurosci (2015)

Bottom Line: We developed a recurrent, firing-rate network model, which detects frequency-change-direction of successively played stimuli and successfully accounts for the context-dependent perception demonstrated in behavioral experiments.The model's network architecture and slow facilitating inhibition emerge as predictions of neuronal mechanisms for these perceptual dynamics.Since the model structure does not depend on the specific stimuli, we show that it generalizes to other contextual effects and stimulus types.

View Article: PubMed Central - PubMed

Affiliation: Courant Institute of Mathematical Sciences, New York University New York, NY, USA.

ABSTRACT
Many natural stimuli have perceptual ambiguities that can be cognitively resolved by the surrounding context. In audition, preceding context can bias the perception of speech and non-speech stimuli. Here, we develop a neuronal network model that can account for how context affects the perception of pitch change between a pair of successive complex tones. We focus especially on an ambiguous comparison-listeners experience opposite percepts (either ascending or descending) for an ambiguous tone pair depending on the spectral location of preceding context tones. We developed a recurrent, firing-rate network model, which detects frequency-change-direction of successively played stimuli and successfully accounts for the context-dependent perception demonstrated in behavioral experiments. The model consists of two tonotopically organized, excitatory populations, E up and E down, that respond preferentially to ascending or descending stimuli in pitch, respectively. These preferences are generated by an inhibitory population that provides inhibition asymmetric in frequency to the two populations; context dependence arises from slow facilitation of inhibition. We show that contextual influence depends on the spectral distribution of preceding tones and the tuning width of inhibitory neurons. Further, we demonstrate, using phase-space analysis, how the facilitated inhibition from previous stimuli and the waning inhibition from the just-preceding tone shape the competition between the E up and E down populations. In sum, our model accounts for contextual influences on the pitch change perception of an ambiguous tone pair by introducing a novel decoding strategy based on direction-selective units. The model's network architecture and slow facilitating inhibition emerge as predictions of neuronal mechanisms for these perceptual dynamics. Since the model structure does not depend on the specific stimuli, we show that it generalizes to other contextual effects and stimulus types.

No MeSH data available.


Related in: MedlinePlus

Frequency shift detection for spectrally non-periodic stimuli. (A) An example of input stimuli. A chord of six synchronous pure tones equally spaced along the logarithmic frequency scale is followed by a test pure tone. The interval between adjacent components in the chord is 0.5 octaves. The ordinate is frequency relative to the lowest component of the chord. The second tone is 0.1 octaves higher than the third lowest component in the chord. (B)Eup shows larger response than Edown to the second tone, indicating a perceived upward shift of frequency. (C) Mean relative response difference,(D) (Equation 6, see Materials and Methods), is largest when the frequency shift is about 0.1 octaves for both intervals, 0.5 octaves (dashed), and 1.0 octaves (solid). Results are averaged for frequency shift relative to “inner” components (2–5) of the chord. There is little variation in the profile in (C) for different inner components. The shape of the tuning curve for frequency shift is qualitatively the same as that measured in psychophysical experiments (Demany et al., 2009, Figures 1C,D).
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4526807&req=5

Figure 8: Frequency shift detection for spectrally non-periodic stimuli. (A) An example of input stimuli. A chord of six synchronous pure tones equally spaced along the logarithmic frequency scale is followed by a test pure tone. The interval between adjacent components in the chord is 0.5 octaves. The ordinate is frequency relative to the lowest component of the chord. The second tone is 0.1 octaves higher than the third lowest component in the chord. (B)Eup shows larger response than Edown to the second tone, indicating a perceived upward shift of frequency. (C) Mean relative response difference,(D) (Equation 6, see Materials and Methods), is largest when the frequency shift is about 0.1 octaves for both intervals, 0.5 octaves (dashed), and 1.0 octaves (solid). Results are averaged for frequency shift relative to “inner” components (2–5) of the chord. There is little variation in the profile in (C) for different inner components. The shape of the tuning curve for frequency shift is qualitatively the same as that measured in psychophysical experiments (Demany et al., 2009, Figures 1C,D).

Mentions: The local comparison property of the model provides a neuronal-based explanation for the experiments by (Demany and Ramos, 2005; Demany et al., 2009). Each sound stimulus was a chord of six synchronously played pure tones, whose frequencies were equally spaced on a logarithmic scale, followed by a test pure tone (Figure 8A). Subjects were asked to compare the test pure tone with the chord in pitch height without knowing which component of the chord should be the basis for their comparison. They found that subjects were most sensitive to a one semitone change in frequency between the test pure tone and one of the chord components (Demany et al., 2009, see Figure 1). Our model can be considered a neuromechanistic implementation of their hypothesis of frequency shift detectors. The model gives larger firing rates of Eup, for example, when the test tone is 0.1 octaves above the third lowest frequency component of the chord (Figures 8A,B), predicting an ascending percept. The dependence of response difference (D) on frequency shift (Figure 8C) resembles the psychometric tuning curves of frequency shift detectors measured by Demany et al. (2009) (see Figure 1). Our model shows maximum response difference (D), corresponding to the highest sensitivity of human subjects, for a frequency shift of about 0.1 octaves for two different spectral intervals (0.5 and 1.0 octaves) separating components of the chord (Figure 8C).


A neuronal network model for context-dependence of pitch change perception.

Huang C, Englitz B, Shamma S, Rinzel J - Front Comput Neurosci (2015)

Frequency shift detection for spectrally non-periodic stimuli. (A) An example of input stimuli. A chord of six synchronous pure tones equally spaced along the logarithmic frequency scale is followed by a test pure tone. The interval between adjacent components in the chord is 0.5 octaves. The ordinate is frequency relative to the lowest component of the chord. The second tone is 0.1 octaves higher than the third lowest component in the chord. (B)Eup shows larger response than Edown to the second tone, indicating a perceived upward shift of frequency. (C) Mean relative response difference,(D) (Equation 6, see Materials and Methods), is largest when the frequency shift is about 0.1 octaves for both intervals, 0.5 octaves (dashed), and 1.0 octaves (solid). Results are averaged for frequency shift relative to “inner” components (2–5) of the chord. There is little variation in the profile in (C) for different inner components. The shape of the tuning curve for frequency shift is qualitatively the same as that measured in psychophysical experiments (Demany et al., 2009, Figures 1C,D).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4526807&req=5

Figure 8: Frequency shift detection for spectrally non-periodic stimuli. (A) An example of input stimuli. A chord of six synchronous pure tones equally spaced along the logarithmic frequency scale is followed by a test pure tone. The interval between adjacent components in the chord is 0.5 octaves. The ordinate is frequency relative to the lowest component of the chord. The second tone is 0.1 octaves higher than the third lowest component in the chord. (B)Eup shows larger response than Edown to the second tone, indicating a perceived upward shift of frequency. (C) Mean relative response difference,(D) (Equation 6, see Materials and Methods), is largest when the frequency shift is about 0.1 octaves for both intervals, 0.5 octaves (dashed), and 1.0 octaves (solid). Results are averaged for frequency shift relative to “inner” components (2–5) of the chord. There is little variation in the profile in (C) for different inner components. The shape of the tuning curve for frequency shift is qualitatively the same as that measured in psychophysical experiments (Demany et al., 2009, Figures 1C,D).
Mentions: The local comparison property of the model provides a neuronal-based explanation for the experiments by (Demany and Ramos, 2005; Demany et al., 2009). Each sound stimulus was a chord of six synchronously played pure tones, whose frequencies were equally spaced on a logarithmic scale, followed by a test pure tone (Figure 8A). Subjects were asked to compare the test pure tone with the chord in pitch height without knowing which component of the chord should be the basis for their comparison. They found that subjects were most sensitive to a one semitone change in frequency between the test pure tone and one of the chord components (Demany et al., 2009, see Figure 1). Our model can be considered a neuromechanistic implementation of their hypothesis of frequency shift detectors. The model gives larger firing rates of Eup, for example, when the test tone is 0.1 octaves above the third lowest frequency component of the chord (Figures 8A,B), predicting an ascending percept. The dependence of response difference (D) on frequency shift (Figure 8C) resembles the psychometric tuning curves of frequency shift detectors measured by Demany et al. (2009) (see Figure 1). Our model shows maximum response difference (D), corresponding to the highest sensitivity of human subjects, for a frequency shift of about 0.1 octaves for two different spectral intervals (0.5 and 1.0 octaves) separating components of the chord (Figure 8C).

Bottom Line: We developed a recurrent, firing-rate network model, which detects frequency-change-direction of successively played stimuli and successfully accounts for the context-dependent perception demonstrated in behavioral experiments.The model's network architecture and slow facilitating inhibition emerge as predictions of neuronal mechanisms for these perceptual dynamics.Since the model structure does not depend on the specific stimuli, we show that it generalizes to other contextual effects and stimulus types.

View Article: PubMed Central - PubMed

Affiliation: Courant Institute of Mathematical Sciences, New York University New York, NY, USA.

ABSTRACT
Many natural stimuli have perceptual ambiguities that can be cognitively resolved by the surrounding context. In audition, preceding context can bias the perception of speech and non-speech stimuli. Here, we develop a neuronal network model that can account for how context affects the perception of pitch change between a pair of successive complex tones. We focus especially on an ambiguous comparison-listeners experience opposite percepts (either ascending or descending) for an ambiguous tone pair depending on the spectral location of preceding context tones. We developed a recurrent, firing-rate network model, which detects frequency-change-direction of successively played stimuli and successfully accounts for the context-dependent perception demonstrated in behavioral experiments. The model consists of two tonotopically organized, excitatory populations, E up and E down, that respond preferentially to ascending or descending stimuli in pitch, respectively. These preferences are generated by an inhibitory population that provides inhibition asymmetric in frequency to the two populations; context dependence arises from slow facilitation of inhibition. We show that contextual influence depends on the spectral distribution of preceding tones and the tuning width of inhibitory neurons. Further, we demonstrate, using phase-space analysis, how the facilitated inhibition from previous stimuli and the waning inhibition from the just-preceding tone shape the competition between the E up and E down populations. In sum, our model accounts for contextual influences on the pitch change perception of an ambiguous tone pair by introducing a novel decoding strategy based on direction-selective units. The model's network architecture and slow facilitating inhibition emerge as predictions of neuronal mechanisms for these perceptual dynamics. Since the model structure does not depend on the specific stimuli, we show that it generalizes to other contextual effects and stimulus types.

No MeSH data available.


Related in: MedlinePlus