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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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Same format as in Figure 5—figure supplement 1, but results are shown for the selective decoding model.Top row: simulations for four different values of the Readout Index (RI). Bottom row: A bimodal distribution of CP is only seen for RI values >= 0.8, and CP of congruent cells is independent of RI.DOI:http://dx.doi.org/10.7554/eLife.02670.015
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fig5s2: Same format as in Figure 5—figure supplement 1, but results are shown for the selective decoding model.Top row: simulations for four different values of the Readout Index (RI). Bottom row: A bimodal distribution of CP is only seen for RI values >= 0.8, and CP of congruent cells is independent of RI.DOI:http://dx.doi.org/10.7554/eLife.02670.015

Mentions: Additional analyses suggest that this difference in the shape of the CP distribution between models is fairly robust to variations in the key model parameters. For the pure correlation model, a bimodal distribution of CPs in the combined condition is a robust result over a range of values of avestibular, including values much smaller than needed to fit our noise correlation data (Figure 5—figure supplement 1). For the selective decoding model, a unimodal distribution of CPs is predicted for a wide range of RI values, and significant bimodality only occurs for RI values ≥0.8, which are not consistent with behavioral thresholds as described below (Figure 5—figure supplement 2).


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

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

Same format as in Figure 5—figure supplement 1, but results are shown for the selective decoding model.Top row: simulations for four different values of the Readout Index (RI). Bottom row: A bimodal distribution of CP is only seen for RI values >= 0.8, and CP of congruent cells is independent of RI.DOI:http://dx.doi.org/10.7554/eLife.02670.015
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig5s2: Same format as in Figure 5—figure supplement 1, but results are shown for the selective decoding model.Top row: simulations for four different values of the Readout Index (RI). Bottom row: A bimodal distribution of CP is only seen for RI values >= 0.8, and CP of congruent cells is independent of RI.DOI:http://dx.doi.org/10.7554/eLife.02670.015
Mentions: Additional analyses suggest that this difference in the shape of the CP distribution between models is fairly robust to variations in the key model parameters. For the pure correlation model, a bimodal distribution of CPs in the combined condition is a robust result over a range of values of avestibular, including values much smaller than needed to fit our noise correlation data (Figure 5—figure supplement 1). For the selective decoding model, a unimodal distribution of CPs is predicted for a wide range of RI values, and significant bimodality only occurs for RI values ≥0.8, which are not consistent with behavioral thresholds as described below (Figure 5—figure supplement 2).

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