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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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Related in: MedlinePlus

Bimodality of CP for opposite cells in the cue combined condition.Top panels: four examples of simulations of the pure-correlation model having different values of avestibular ranging from 0.01 to 0.12. Both the magnitude and bimodality of the CP distribution increases with avestibular. To quantify bimodality, we chose cells with Congruency Index in the range [−1–0.5], and applied K-means clustering to generate two clusters. The vertical distance, d, between the centroids of the two clusters was taken as an index of the separation of the two clusters. Bottom row: (left) Summary of d values as a function of avestibular. Asterisks represent significant bimodality, as assessed by a modality test (‘Materials and methods’). (right) Relationship between the CP of congruent cells (congruency index from 0.5 to 1) and avestibular.DOI:http://dx.doi.org/10.7554/eLife.02670.014
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fig5s1: Bimodality of CP for opposite cells in the cue combined condition.Top panels: four examples of simulations of the pure-correlation model having different values of avestibular ranging from 0.01 to 0.12. Both the magnitude and bimodality of the CP distribution increases with avestibular. To quantify bimodality, we chose cells with Congruency Index in the range [−1–0.5], and applied K-means clustering to generate two clusters. The vertical distance, d, between the centroids of the two clusters was taken as an index of the separation of the two clusters. Bottom row: (left) Summary of d values as a function of avestibular. Asterisks represent significant bimodality, as assessed by a modality test (‘Materials and methods’). (right) Relationship between the CP of congruent cells (congruency index from 0.5 to 1) and avestibular.DOI:http://dx.doi.org/10.7554/eLife.02670.014

Mentions: 10.7554/eLife.02670.015Figure 5—figure supplement 2.Same format as in Figure 5—figure supplement 1, but results are shown for the selective decoding model.


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

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

Bimodality of CP for opposite cells in the cue combined condition.Top panels: four examples of simulations of the pure-correlation model having different values of avestibular ranging from 0.01 to 0.12. Both the magnitude and bimodality of the CP distribution increases with avestibular. To quantify bimodality, we chose cells with Congruency Index in the range [−1–0.5], and applied K-means clustering to generate two clusters. The vertical distance, d, between the centroids of the two clusters was taken as an index of the separation of the two clusters. Bottom row: (left) Summary of d values as a function of avestibular. Asterisks represent significant bimodality, as assessed by a modality test (‘Materials and methods’). (right) Relationship between the CP of congruent cells (congruency index from 0.5 to 1) and avestibular.DOI:http://dx.doi.org/10.7554/eLife.02670.014
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig5s1: Bimodality of CP for opposite cells in the cue combined condition.Top panels: four examples of simulations of the pure-correlation model having different values of avestibular ranging from 0.01 to 0.12. Both the magnitude and bimodality of the CP distribution increases with avestibular. To quantify bimodality, we chose cells with Congruency Index in the range [−1–0.5], and applied K-means clustering to generate two clusters. The vertical distance, d, between the centroids of the two clusters was taken as an index of the separation of the two clusters. Bottom row: (left) Summary of d values as a function of avestibular. Asterisks represent significant bimodality, as assessed by a modality test (‘Materials and methods’). (right) Relationship between the CP of congruent cells (congruency index from 0.5 to 1) and avestibular.DOI:http://dx.doi.org/10.7554/eLife.02670.014
Mentions: 10.7554/eLife.02670.015Figure 5—figure supplement 2.Same format as in Figure 5—figure supplement 1, but results are shown for the selective decoding model.

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