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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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Noise correlation structure of the pure correlation model computed from the signal correlations of all distinct pairings of 129 neurons that were recorded previously by Gu et al. (2011).For the pure-correlation model, correlated noise is determined by the signal correlation in the vestibular condition, such that pairs of cells with matched (black) and mismatched (gray) congruency have opposite relationships between rnoise and rsignal in the visual condition.DOI:http://dx.doi.org/10.7554/eLife.02670.010
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fig3s2: Noise correlation structure of the pure correlation model computed from the signal correlations of all distinct pairings of 129 neurons that were recorded previously by Gu et al. (2011).For the pure-correlation model, correlated noise is determined by the signal correlation in the vestibular condition, such that pairs of cells with matched (black) and mismatched (gray) congruency have opposite relationships between rnoise and rsignal in the visual condition.DOI:http://dx.doi.org/10.7554/eLife.02670.010


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

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

Noise correlation structure of the pure correlation model computed from the signal correlations of all distinct pairings of 129 neurons that were recorded previously by Gu et al. (2011).For the pure-correlation model, correlated noise is determined by the signal correlation in the vestibular condition, such that pairs of cells with matched (black) and mismatched (gray) congruency have opposite relationships between rnoise and rsignal in the visual condition.DOI:http://dx.doi.org/10.7554/eLife.02670.010
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3s2: Noise correlation structure of the pure correlation model computed from the signal correlations of all distinct pairings of 129 neurons that were recorded previously by Gu et al. (2011).For the pure-correlation model, correlated noise is determined by the signal correlation in the vestibular condition, such that pairs of cells with matched (black) and mismatched (gray) congruency have opposite relationships between rnoise and rsignal in the visual condition.DOI:http://dx.doi.org/10.7554/eLife.02670.010
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