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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.

No MeSH data available.


Related in: MedlinePlus

Schematic of the connectivity in the neuronal network model. The network model consists of two excitatory populations (Eup and Edown) and an inhibitory population (I), tonotopically organized. The asymmetric inhibitory feedback leads to an ascending/descending frequency change preference for the Eup and Edown populations, respectively. Each unit is a local subpopulation, positioned at its characteristic frequency (CF). Activity of each unit is described by a firing rate, whose dynamics are governed by the differential equations (see Equation 2 in Materials and Methods). Red arrows signify recurrent excitation and blue arrows inhibition. The subset of the connections shown illustrates the architecture's qualitative nature: the synaptic footprints from E to E and from E to I are narrow and symmetric; from I to E the footprint is broad and asymmetric.
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Figure 2: Schematic of the connectivity in the neuronal network model. The network model consists of two excitatory populations (Eup and Edown) and an inhibitory population (I), tonotopically organized. The asymmetric inhibitory feedback leads to an ascending/descending frequency change preference for the Eup and Edown populations, respectively. Each unit is a local subpopulation, positioned at its characteristic frequency (CF). Activity of each unit is described by a firing rate, whose dynamics are governed by the differential equations (see Equation 2 in Materials and Methods). Red arrows signify recurrent excitation and blue arrows inhibition. The subset of the connections shown illustrates the architecture's qualitative nature: the synaptic footprints from E to E and from E to I are narrow and symmetric; from I to E the footprint is broad and asymmetric.

Mentions: Our network model consists of three tonotopically organized subpopulations: two excitatory (E) populations that drive a common inhibitory (I) population and the latter provides recurrent inhibition but with oppositely directed asymmetric projective fields (ωup, ωdown) (see schematic in Figure 2). The model describes the firing rate dynamics of three populations as a continuum in frequency, where each location in frequency corresponds to a neuron with this location as its characteristic frequency (CF).


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

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

Schematic of the connectivity in the neuronal network model. The network model consists of two excitatory populations (Eup and Edown) and an inhibitory population (I), tonotopically organized. The asymmetric inhibitory feedback leads to an ascending/descending frequency change preference for the Eup and Edown populations, respectively. Each unit is a local subpopulation, positioned at its characteristic frequency (CF). Activity of each unit is described by a firing rate, whose dynamics are governed by the differential equations (see Equation 2 in Materials and Methods). Red arrows signify recurrent excitation and blue arrows inhibition. The subset of the connections shown illustrates the architecture's qualitative nature: the synaptic footprints from E to E and from E to I are narrow and symmetric; from I to E the footprint is broad and asymmetric.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 2: Schematic of the connectivity in the neuronal network model. The network model consists of two excitatory populations (Eup and Edown) and an inhibitory population (I), tonotopically organized. The asymmetric inhibitory feedback leads to an ascending/descending frequency change preference for the Eup and Edown populations, respectively. Each unit is a local subpopulation, positioned at its characteristic frequency (CF). Activity of each unit is described by a firing rate, whose dynamics are governed by the differential equations (see Equation 2 in Materials and Methods). Red arrows signify recurrent excitation and blue arrows inhibition. The subset of the connections shown illustrates the architecture's qualitative nature: the synaptic footprints from E to E and from E to I are narrow and symmetric; from I to E the footprint is broad and asymmetric.
Mentions: Our network model consists of three tonotopically organized subpopulations: two excitatory (E) populations that drive a common inhibitory (I) population and the latter provides recurrent inhibition but with oppositely directed asymmetric projective fields (ωup, ωdown) (see schematic in Figure 2). The model describes the firing rate dynamics of three populations as a continuum in frequency, where each location in frequency corresponds to a neuron with this location as its characteristic frequency (CF).

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