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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.

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Single-unit properties of Eup and Edown. (A) Schematic showing the different sources of inhibitory input to Eup and Edown units. (B) Tuning curves of Eup (blue solid) and Edown (green solid) units (at PC = 6 st) are skewed in different directions. Larger skewness is seen when the tuning curves (dashed) are calculated for a different parameter set with broader input. The input drive for a tone is modeled as a sustained Gaussian function centered at the pitch class of that tone (Equation 1). The tuning curve shows peak amplitude of firing rate during the stimulus duration (100 ms). (C) A preceding tone influences the neural activity to the next tone via asymmetric inhibition. Color represents the peak amplitude of firing rate of an Eup unit (PC = 6 st) during T2 for different combinations of sequential stimuli T1 and T2. A Shepard tone of random pitch class is presented before T1 for random initial conditions and plotted results are averaged over 10 runs. (D) Plot as in (C) for an Edown unit at the same location (PC = 6 st).
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Figure 4: Single-unit properties of Eup and Edown. (A) Schematic showing the different sources of inhibitory input to Eup and Edown units. (B) Tuning curves of Eup (blue solid) and Edown (green solid) units (at PC = 6 st) are skewed in different directions. Larger skewness is seen when the tuning curves (dashed) are calculated for a different parameter set with broader input. The input drive for a tone is modeled as a sustained Gaussian function centered at the pitch class of that tone (Equation 1). The tuning curve shows peak amplitude of firing rate during the stimulus duration (100 ms). (C) A preceding tone influences the neural activity to the next tone via asymmetric inhibition. Color represents the peak amplitude of firing rate of an Eup unit (PC = 6 st) during T2 for different combinations of sequential stimuli T1 and T2. A Shepard tone of random pitch class is presented before T1 for random initial conditions and plotted results are averaged over 10 runs. (D) Plot as in (C) for an Edown unit at the same location (PC = 6 st).

Mentions: The direction-selective excitatory neurons exhibit non-symmetric tuning curves, even without a preceding stimulus (Figure 4). A tuning curve in the present context describes the response properties of a neuron to Shepard tones of any PC. Since an Eup unit receives inhibition from the higher frequency side (Figure 4A), tones above the unit's PC invoke more inhibition on this Eup unit, resulting in lower firing rates than tones at lower PC. Conversely, an Edown unit is inhibited from the lower frequency side, thus responding stronger to tones above its PC. Hence, the tuning curve of Eup units leans to lower PC's (positive skewness, Figure 4B blue) and the opposite for Edown units (negative skewness, Figure 4B green). In this example, both units receive the same input with Gaussian weight centered at 6 st (see Materials and Methods, Equation 1).


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

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

Single-unit properties of Eup and Edown. (A) Schematic showing the different sources of inhibitory input to Eup and Edown units. (B) Tuning curves of Eup (blue solid) and Edown (green solid) units (at PC = 6 st) are skewed in different directions. Larger skewness is seen when the tuning curves (dashed) are calculated for a different parameter set with broader input. The input drive for a tone is modeled as a sustained Gaussian function centered at the pitch class of that tone (Equation 1). The tuning curve shows peak amplitude of firing rate during the stimulus duration (100 ms). (C) A preceding tone influences the neural activity to the next tone via asymmetric inhibition. Color represents the peak amplitude of firing rate of an Eup unit (PC = 6 st) during T2 for different combinations of sequential stimuli T1 and T2. A Shepard tone of random pitch class is presented before T1 for random initial conditions and plotted results are averaged over 10 runs. (D) Plot as in (C) for an Edown unit at the same location (PC = 6 st).
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Figure 4: Single-unit properties of Eup and Edown. (A) Schematic showing the different sources of inhibitory input to Eup and Edown units. (B) Tuning curves of Eup (blue solid) and Edown (green solid) units (at PC = 6 st) are skewed in different directions. Larger skewness is seen when the tuning curves (dashed) are calculated for a different parameter set with broader input. The input drive for a tone is modeled as a sustained Gaussian function centered at the pitch class of that tone (Equation 1). The tuning curve shows peak amplitude of firing rate during the stimulus duration (100 ms). (C) A preceding tone influences the neural activity to the next tone via asymmetric inhibition. Color represents the peak amplitude of firing rate of an Eup unit (PC = 6 st) during T2 for different combinations of sequential stimuli T1 and T2. A Shepard tone of random pitch class is presented before T1 for random initial conditions and plotted results are averaged over 10 runs. (D) Plot as in (C) for an Edown unit at the same location (PC = 6 st).
Mentions: The direction-selective excitatory neurons exhibit non-symmetric tuning curves, even without a preceding stimulus (Figure 4). A tuning curve in the present context describes the response properties of a neuron to Shepard tones of any PC. Since an Eup unit receives inhibition from the higher frequency side (Figure 4A), tones above the unit's PC invoke more inhibition on this Eup unit, resulting in lower firing rates than tones at lower PC. Conversely, an Edown unit is inhibited from the lower frequency side, thus responding stronger to tones above its PC. Hence, the tuning curve of Eup units leans to lower PC's (positive skewness, Figure 4B blue) and the opposite for Edown units (negative skewness, Figure 4B green). In this example, both units receive the same input with Gaussian weight centered at 6 st (see Materials and Methods, Equation 1).

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