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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

Comparison of neuronal sensitivity between the visual (ordinate) and vestibular (abscissa) stimulus conditions for 30 congruent cells.Arrows and numbers indicate geometric mean values. Overall, visual responses were somewhat more sensitive than vestibular responses, although the animals’ behavioral performance was similar between conditions.DOI:http://dx.doi.org/10.7554/eLife.02670.017
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fig6s1: Comparison of neuronal sensitivity between the visual (ordinate) and vestibular (abscissa) stimulus conditions for 30 congruent cells.Arrows and numbers indicate geometric mean values. Overall, visual responses were somewhat more sensitive than vestibular responses, although the animals’ behavioral performance was similar between conditions.DOI:http://dx.doi.org/10.7554/eLife.02670.017

Mentions: Unlike the animals' behavior, decoder performance based on the pure-correlation model predicted mismatched thresholds for the visual and vestibular conditions (Figure 6A, open symbols and solid black curve). The average threshold predicted for the visual condition (1.24° ± 0.02°, mean ± SEM) was 42% lower than that for the vestibular condition (2.16° ± 0.14°,mean ± SEM). This may be due to the fact that the average neuronal threshold of congruent MSTd neurons tends to be lower for the visual condition (5.5°) than the vestibular condition (7.1°), although this difference did not reach significance (p=0.106, t test, N = 30, Figure 6—figure supplement 1). Despite the performance mismatch between the two single-cue conditions, sensitivity was still improved during the combined condition (1.10° ± 0.05°), as expected by optimal cue integration predictions (1.07° ± 0.02°).


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 neuronal sensitivity between the visual (ordinate) and vestibular (abscissa) stimulus conditions for 30 congruent cells.Arrows and numbers indicate geometric mean values. Overall, visual responses were somewhat more sensitive than vestibular responses, although the animals’ behavioral performance was similar between conditions.DOI:http://dx.doi.org/10.7554/eLife.02670.017
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig6s1: Comparison of neuronal sensitivity between the visual (ordinate) and vestibular (abscissa) stimulus conditions for 30 congruent cells.Arrows and numbers indicate geometric mean values. Overall, visual responses were somewhat more sensitive than vestibular responses, although the animals’ behavioral performance was similar between conditions.DOI:http://dx.doi.org/10.7554/eLife.02670.017
Mentions: Unlike the animals' behavior, decoder performance based on the pure-correlation model predicted mismatched thresholds for the visual and vestibular conditions (Figure 6A, open symbols and solid black curve). The average threshold predicted for the visual condition (1.24° ± 0.02°, mean ± SEM) was 42% lower than that for the vestibular condition (2.16° ± 0.14°,mean ± SEM). This may be due to the fact that the average neuronal threshold of congruent MSTd neurons tends to be lower for the visual condition (5.5°) than the vestibular condition (7.1°), although this difference did not reach significance (p=0.106, t test, N = 30, Figure 6—figure supplement 1). Despite the performance mismatch between the two single-cue conditions, sensitivity was still improved during the combined condition (1.10° ± 0.05°), as expected by optimal cue integration predictions (1.07° ± 0.02°).

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