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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.


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3-variable winner-take-all model. We devised a 3-variable model, without frequency dependence, to analyze the biasing mechanism of the competition between Eup and Edown populations. (A) The model, represented by this schematic, consists of two excitatory populations, with firing rates Eu and Ed, that are inhibited by a global inhibitory population I with weights ωiu and ωid, respectively (see Materials and Methods). Inhibition is without dynamic facilitation. (B,C) Phase plane analysis (see Materials and Methods). We project the phase space onto the plane of Eu and Ed. Null-clines (where rate of change is zero) of Eu (blue) and Ed (green) are calculated by assuming I acts instantaneously. (B) When ωiu = ωid, there are three steady states (U, D, S). Trajectory (dotted) converges to the U state if Eu is larger than Ed initially [magenta, initial condition (Eu(0), Ed(0), I(0)) = (0.3, 0, 0)] and approaches the S state if Eu and Ed are equal, initially [red, (Eu(0), Ed(0), I(0)) = (0, 0, 0)]. (C) When ωiu < ωid, there is only one steady state. The trajectory converges to the U state even if Eu equals Ed initially [red, (Eu(0), Ed(0), I(0)) = (0, 0, 0)].
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Figure 9: 3-variable winner-take-all model. We devised a 3-variable model, without frequency dependence, to analyze the biasing mechanism of the competition between Eup and Edown populations. (A) The model, represented by this schematic, consists of two excitatory populations, with firing rates Eu and Ed, that are inhibited by a global inhibitory population I with weights ωiu and ωid, respectively (see Materials and Methods). Inhibition is without dynamic facilitation. (B,C) Phase plane analysis (see Materials and Methods). We project the phase space onto the plane of Eu and Ed. Null-clines (where rate of change is zero) of Eu (blue) and Ed (green) are calculated by assuming I acts instantaneously. (B) When ωiu = ωid, there are three steady states (U, D, S). Trajectory (dotted) converges to the U state if Eu is larger than Ed initially [magenta, initial condition (Eu(0), Ed(0), I(0)) = (0.3, 0, 0)] and approaches the S state if Eu and Ed are equal, initially [red, (Eu(0), Ed(0), I(0)) = (0, 0, 0)]. (C) When ωiu < ωid, there is only one steady state. The trajectory converges to the U state even if Eu equals Ed initially [red, (Eu(0), Ed(0), I(0)) = (0, 0, 0)].

Mentions: By assuming rapid recruitment of I units (I-activity, an instantaneous function of inputs) we can project the state space onto the phase plane of Eu and Ed. When ωiu = ωid, there are three steady states: the U state (up-dominant) where Eu > Ed, the D state (down-dominant) where Eu < Ed and the S state (symmetric) where Eu = Ed. The U and D states are stable, while the S state is a saddle point. This is the phase plane of competition dynamics. If Eu and Ed start off as identical, the solution trajectory is symmetric and converges to the S state if there are no fluctuations (Figure 9B, red), while the U state is approached if Eu is higher, initially (Figure 9B, magenta). On the other hand, suppose that ωiu < ωid, as would occur if ωid were facilitated by preceding lower frequency tones. In this case, the competition is biased toward Eu such that only the U state remains and the solution converges to the U state for any initial condition (Figure 9C, red). This shows that initial conditions and inhibitory synaptic strengths can both bias the competition between Eu and Ed.


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

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

3-variable winner-take-all model. We devised a 3-variable model, without frequency dependence, to analyze the biasing mechanism of the competition between Eup and Edown populations. (A) The model, represented by this schematic, consists of two excitatory populations, with firing rates Eu and Ed, that are inhibited by a global inhibitory population I with weights ωiu and ωid, respectively (see Materials and Methods). Inhibition is without dynamic facilitation. (B,C) Phase plane analysis (see Materials and Methods). We project the phase space onto the plane of Eu and Ed. Null-clines (where rate of change is zero) of Eu (blue) and Ed (green) are calculated by assuming I acts instantaneously. (B) When ωiu = ωid, there are three steady states (U, D, S). Trajectory (dotted) converges to the U state if Eu is larger than Ed initially [magenta, initial condition (Eu(0), Ed(0), I(0)) = (0.3, 0, 0)] and approaches the S state if Eu and Ed are equal, initially [red, (Eu(0), Ed(0), I(0)) = (0, 0, 0)]. (C) When ωiu < ωid, there is only one steady state. The trajectory converges to the U state even if Eu equals Ed initially [red, (Eu(0), Ed(0), I(0)) = (0, 0, 0)].
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4526807&req=5

Figure 9: 3-variable winner-take-all model. We devised a 3-variable model, without frequency dependence, to analyze the biasing mechanism of the competition between Eup and Edown populations. (A) The model, represented by this schematic, consists of two excitatory populations, with firing rates Eu and Ed, that are inhibited by a global inhibitory population I with weights ωiu and ωid, respectively (see Materials and Methods). Inhibition is without dynamic facilitation. (B,C) Phase plane analysis (see Materials and Methods). We project the phase space onto the plane of Eu and Ed. Null-clines (where rate of change is zero) of Eu (blue) and Ed (green) are calculated by assuming I acts instantaneously. (B) When ωiu = ωid, there are three steady states (U, D, S). Trajectory (dotted) converges to the U state if Eu is larger than Ed initially [magenta, initial condition (Eu(0), Ed(0), I(0)) = (0.3, 0, 0)] and approaches the S state if Eu and Ed are equal, initially [red, (Eu(0), Ed(0), I(0)) = (0, 0, 0)]. (C) When ωiu < ωid, there is only one steady state. The trajectory converges to the U state even if Eu equals Ed initially [red, (Eu(0), Ed(0), I(0)) = (0, 0, 0)].
Mentions: By assuming rapid recruitment of I units (I-activity, an instantaneous function of inputs) we can project the state space onto the phase plane of Eu and Ed. When ωiu = ωid, there are three steady states: the U state (up-dominant) where Eu > Ed, the D state (down-dominant) where Eu < Ed and the S state (symmetric) where Eu = Ed. The U and D states are stable, while the S state is a saddle point. This is the phase plane of competition dynamics. If Eu and Ed start off as identical, the solution trajectory is symmetric and converges to the S state if there are no fluctuations (Figure 9B, red), while the U state is approached if Eu is higher, initially (Figure 9B, magenta). On the other hand, suppose that ωiu < ωid, as would occur if ωid were facilitated by preceding lower frequency tones. In this case, the competition is biased toward Eu such that only the U state remains and the solution converges to the U state for any initial condition (Figure 9C, red). This shows that initial conditions and inhibitory synaptic strengths can both bias the competition between Eu and Ed.

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