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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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Comparison of vestibular and visual signal correlations for 127 pairs of neurons simultaneously recorded from area MSTd by Gu et al. (2011).There is a fairly weak correlation between the two variables (R = 0.29, p=0.001, Spearman correlation). Histograms along the top and right side show marginal distributions of the two signal correlations.DOI:http://dx.doi.org/10.7554/eLife.02670.009
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fig3s1: Comparison of vestibular and visual signal correlations for 127 pairs of neurons simultaneously recorded from area MSTd by Gu et al. (2011).There is a fairly weak correlation between the two variables (R = 0.29, p=0.001, Spearman correlation). Histograms along the top and right side show marginal distributions of the two signal correlations.DOI:http://dx.doi.org/10.7554/eLife.02670.009

Mentions: Which model best matches experimental data on correlated noise? Our previous study (Gu et al., 2011) showed that rnoise measured for pairs of MSTd neurons depended approximately equally on rsignal computed from both visual and vestibular tuning curves (Figure 3A). This dependence on rsignal for both modalities is not due to strong covariance between the two signal correlations because rsignal values for visual and vestibular tuning are only weakly correlated (Figure 3—figure supplement 1). Note also that noise correlations are generally negative for neurons with opposite tuning (negative signal correlations) in our heading discrimination data sets (Gu et al., 2011; Chen et al., 2013; Liu et al., 2013), such that subtracting responses of oppositely tuned neurons is not expected to have the benefit seen in other systems (Romo et al., 2003).10.7554/eLife.02670.008Figure 3.Comparison of the structure of correlated noise between models and data.


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

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

Comparison of vestibular and visual signal correlations for 127 pairs of neurons simultaneously recorded from area MSTd by Gu et al. (2011).There is a fairly weak correlation between the two variables (R = 0.29, p=0.001, Spearman correlation). Histograms along the top and right side show marginal distributions of the two signal correlations.DOI:http://dx.doi.org/10.7554/eLife.02670.009
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4109308&req=5

fig3s1: Comparison of vestibular and visual signal correlations for 127 pairs of neurons simultaneously recorded from area MSTd by Gu et al. (2011).There is a fairly weak correlation between the two variables (R = 0.29, p=0.001, Spearman correlation). Histograms along the top and right side show marginal distributions of the two signal correlations.DOI:http://dx.doi.org/10.7554/eLife.02670.009
Mentions: Which model best matches experimental data on correlated noise? Our previous study (Gu et al., 2011) showed that rnoise measured for pairs of MSTd neurons depended approximately equally on rsignal computed from both visual and vestibular tuning curves (Figure 3A). This dependence on rsignal for both modalities is not due to strong covariance between the two signal correlations because rsignal values for visual and vestibular tuning are only weakly correlated (Figure 3—figure supplement 1). Note also that noise correlations are generally negative for neurons with opposite tuning (negative signal correlations) in our heading discrimination data sets (Gu et al., 2011; Chen et al., 2013; Liu et al., 2013), such that subtracting responses of oppositely tuned neurons is not expected to have the benefit seen in other systems (Romo et al., 2003).10.7554/eLife.02670.008Figure 3.Comparison of the structure of correlated noise between models and data.

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