Limits...
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.

Show MeSH

Related in: MedlinePlus

Predictions from a variant of the pure-correlation model in which correlated noise depends only on signal correlations from the visual tuning curves.This modification reverses the patterns of CPs across stimulus conditions, as compared to Figure 2C,D. This pattern of results is not consistent with experimental data.DOI:http://dx.doi.org/10.7554/eLife.02670.005
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4109308&req=5

fig2s1: Predictions from a variant of the pure-correlation model in which correlated noise depends only on signal correlations from the visual tuning curves.This modification reverses the patterns of CPs across stimulus conditions, as compared to Figure 2C,D. This pattern of results is not consistent with experimental data.DOI:http://dx.doi.org/10.7554/eLife.02670.005

Mentions: In contrast, if correlated noise depends on the similarity of visual heading preferences, then the pattern of results will be reversed for the vestibular and visual conditions (Figure 2—figure supplement 1), which is clearly inconsistent with the experimental data (Figure 2B). Finally, correlations between the two pools of the pure-correlation model could depend on the similarity of heading preferences for both the visual and vestibular modalities (‘Materials and methods’; Equation 2). If correlated noise depends equally on both visual and vestibular signal correlations (i.e., Equation 2 with avestibular = avisual), then correlations between opposite cells and congruent cells become effectively zero because the two terms of Equation 2 cancel. In this case, the pure-correlation model becomes equivalent to the selective decoding model with zero weight placed on opposite cells, as considered below.


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

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

Predictions from a variant of the pure-correlation model in which correlated noise depends only on signal correlations from the visual tuning curves.This modification reverses the patterns of CPs across stimulus conditions, as compared to Figure 2C,D. This pattern of results is not consistent with experimental data.DOI:http://dx.doi.org/10.7554/eLife.02670.005
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2s1: Predictions from a variant of the pure-correlation model in which correlated noise depends only on signal correlations from the visual tuning curves.This modification reverses the patterns of CPs across stimulus conditions, as compared to Figure 2C,D. This pattern of results is not consistent with experimental data.DOI:http://dx.doi.org/10.7554/eLife.02670.005
Mentions: In contrast, if correlated noise depends on the similarity of visual heading preferences, then the pattern of results will be reversed for the vestibular and visual conditions (Figure 2—figure supplement 1), which is clearly inconsistent with the experimental data (Figure 2B). Finally, correlations between the two pools of the pure-correlation model could depend on the similarity of heading preferences for both the visual and vestibular modalities (‘Materials and methods’; Equation 2). If correlated noise depends equally on both visual and vestibular signal correlations (i.e., Equation 2 with avestibular = avisual), then correlations between opposite cells and congruent cells become effectively zero because the two terms of Equation 2 cancel. In this case, the pure-correlation model becomes equivalent to the selective decoding model with zero weight placed on opposite cells, as considered below.

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