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Contribution of correlated noise and selective decoding to choice probability measurements in extrastriate visual cortex.

Gu Y, Angelaki DE, DeAngelis GC - Elife (2014)

Bottom Line: We used biologically-constrained simulations to explore this issue, taking advantage of a peculiar pattern of CPs exhibited by multisensory neurons in area MSTd that represent self-motion.Although models that relied on correlated noise or selective decoding could both account for the peculiar pattern of CPs, predictions of the selective decoding model were substantially more consistent with various features of the neural and behavioral data.While correlated noise is essential to observe CPs, our findings suggest that selective decoding of neuronal signals also plays important roles.

View Article: PubMed Central - PubMed

Affiliation: Institute of Neuroscience and Key Laboratory of Primate Neurobiology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.

ABSTRACT
Trial by trial covariations between neural activity and perceptual decisions (quantified by choice Probability, CP) have been used to probe the contribution of sensory neurons to perceptual decisions. CPs are thought to be determined by both selective decoding of neural activity and by the structure of correlated noise among neurons, but the respective roles of these factors in creating CPs have been controversial. We used biologically-constrained simulations to explore this issue, taking advantage of a peculiar pattern of CPs exhibited by multisensory neurons in area MSTd that represent self-motion. Although models that relied on correlated noise or selective decoding could both account for the peculiar pattern of CPs, predictions of the selective decoding model were substantially more consistent with various features of the neural and behavioral data. While correlated noise is essential to observe CPs, our findings suggest that selective decoding of neuronal signals also plays important roles.

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Predictions of choice probabilities from the two models.(A) The pattern of CPs predicted by the pure-correlation model. Format as in Figure 2B. (B) A family of weighting profiles used to consider various degrees of contribution of opposite cells to the selective decoding model. Each curve shows the decoding weight as a function of congruency between visual and vestibular heading tuning. Each curve corresponds to a specific value of the Readout Index (RI). (C) Predicted average CPs from the selective decoding model for a subset of the RI values illustrated in (B). (D) The pattern of CPs across neurons in the selective decoding model for an RI value of 0.5. Cyan symbols: congruent cells; Magenta symbols: opposite cells; Unfilled symbols: intermediate cells. Solid squares: mean CP. Dashed horizontal line: CP = 0.5.DOI:http://dx.doi.org/10.7554/eLife.02670.012
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fig4: Predictions of choice probabilities from the two models.(A) The pattern of CPs predicted by the pure-correlation model. Format as in Figure 2B. (B) A family of weighting profiles used to consider various degrees of contribution of opposite cells to the selective decoding model. Each curve shows the decoding weight as a function of congruency between visual and vestibular heading tuning. Each curve corresponds to a specific value of the Readout Index (RI). (C) Predicted average CPs from the selective decoding model for a subset of the RI values illustrated in (B). (D) The pattern of CPs across neurons in the selective decoding model for an RI value of 0.5. Cyan symbols: congruent cells; Magenta symbols: opposite cells; Unfilled symbols: intermediate cells. Solid squares: mean CP. Dashed horizontal line: CP = 0.5.DOI:http://dx.doi.org/10.7554/eLife.02670.012

Mentions: With our biologically-constrained versions of the pure correlation and selective decoding models, we now consider the patterns of choice probabilities predicted by each model and how they compare to data from MSTd neurons. In the pure-correlation model (Figure 4A), the average CP for congruent cells is significantly greater than 0.5 for both the vestibular (0.65 ± 0.06 SD) and visual (0.65 ± 0.04 SD) conditions (p<0.001, t test). For opposite cells, the average CP is significantly >0.5 in the vestibular condition (0.623 ± 0.039 SD, p<0.001, t test) and significantly <0.5 in the visual condition (0.372 ± 0.047 SD, p<0.001). This pattern of CPs is qualitatively similar to that observed for MSTd neurons (Figure 2B).10.7554/eLife.02670.012Figure 4.Predictions of choice probabilities from the two models.


Contribution of correlated noise and selective decoding to choice probability measurements in extrastriate visual cortex.

Gu Y, Angelaki DE, DeAngelis GC - Elife (2014)

Predictions of choice probabilities from the two models.(A) The pattern of CPs predicted by the pure-correlation model. Format as in Figure 2B. (B) A family of weighting profiles used to consider various degrees of contribution of opposite cells to the selective decoding model. Each curve shows the decoding weight as a function of congruency between visual and vestibular heading tuning. Each curve corresponds to a specific value of the Readout Index (RI). (C) Predicted average CPs from the selective decoding model for a subset of the RI values illustrated in (B). (D) The pattern of CPs across neurons in the selective decoding model for an RI value of 0.5. Cyan symbols: congruent cells; Magenta symbols: opposite cells; Unfilled symbols: intermediate cells. Solid squares: mean CP. Dashed horizontal line: CP = 0.5.DOI:http://dx.doi.org/10.7554/eLife.02670.012
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4109308&req=5

fig4: Predictions of choice probabilities from the two models.(A) The pattern of CPs predicted by the pure-correlation model. Format as in Figure 2B. (B) A family of weighting profiles used to consider various degrees of contribution of opposite cells to the selective decoding model. Each curve shows the decoding weight as a function of congruency between visual and vestibular heading tuning. Each curve corresponds to a specific value of the Readout Index (RI). (C) Predicted average CPs from the selective decoding model for a subset of the RI values illustrated in (B). (D) The pattern of CPs across neurons in the selective decoding model for an RI value of 0.5. Cyan symbols: congruent cells; Magenta symbols: opposite cells; Unfilled symbols: intermediate cells. Solid squares: mean CP. Dashed horizontal line: CP = 0.5.DOI:http://dx.doi.org/10.7554/eLife.02670.012
Mentions: With our biologically-constrained versions of the pure correlation and selective decoding models, we now consider the patterns of choice probabilities predicted by each model and how they compare to data from MSTd neurons. In the pure-correlation model (Figure 4A), the average CP for congruent cells is significantly greater than 0.5 for both the vestibular (0.65 ± 0.06 SD) and visual (0.65 ± 0.04 SD) conditions (p<0.001, t test). For opposite cells, the average CP is significantly >0.5 in the vestibular condition (0.623 ± 0.039 SD, p<0.001, t test) and significantly <0.5 in the visual condition (0.372 ± 0.047 SD, p<0.001). This pattern of CPs is qualitatively similar to that observed for MSTd neurons (Figure 2B).10.7554/eLife.02670.012Figure 4.Predictions of choice probabilities from the two models.

Bottom Line: We used biologically-constrained simulations to explore this issue, taking advantage of a peculiar pattern of CPs exhibited by multisensory neurons in area MSTd that represent self-motion.Although models that relied on correlated noise or selective decoding could both account for the peculiar pattern of CPs, predictions of the selective decoding model were substantially more consistent with various features of the neural and behavioral data.While correlated noise is essential to observe CPs, our findings suggest that selective decoding of neuronal signals also plays important roles.

View Article: PubMed Central - PubMed

Affiliation: Institute of Neuroscience and Key Laboratory of Primate Neurobiology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.

ABSTRACT
Trial by trial covariations between neural activity and perceptual decisions (quantified by choice Probability, CP) have been used to probe the contribution of sensory neurons to perceptual decisions. CPs are thought to be determined by both selective decoding of neural activity and by the structure of correlated noise among neurons, but the respective roles of these factors in creating CPs have been controversial. We used biologically-constrained simulations to explore this issue, taking advantage of a peculiar pattern of CPs exhibited by multisensory neurons in area MSTd that represent self-motion. Although models that relied on correlated noise or selective decoding could both account for the peculiar pattern of CPs, predictions of the selective decoding model were substantially more consistent with various features of the neural and behavioral data. While correlated noise is essential to observe CPs, our findings suggest that selective decoding of neuronal signals also plays important roles.

Show MeSH
Related in: MedlinePlus