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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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Predictions from a variant of the selective decoding model in which responses are decoded according to the visual heading tuning of each neuron, instead of the vestibular tuning.This modification reverses the CP patterns across stimulus conditions, as compared to Figure 2E,F, and is inconsistent with experimental data.DOI:http://dx.doi.org/10.7554/eLife.02670.006
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fig2s2: Predictions from a variant of the selective decoding model in which responses are decoded according to the visual heading tuning of each neuron, instead of the vestibular tuning.This modification reverses the CP patterns across stimulus conditions, as compared to Figure 2E,F, and is inconsistent with experimental data.DOI:http://dx.doi.org/10.7554/eLife.02670.006

Mentions: To examine the predictions of the selective decoding model, we again manipulated two aspects of the readout. With regard to magnitude of the decoding weights, a decoding weight different from zero was essential to produce CPs for opposite cells that were different from the chance level (green symbols/lines vs other colors in Figure 2F). With regard to polarity (or sign) of the decoding weights, decoding responses of opposite cells with respect to their vestibular heading preference led to CP >0.5 in the vestibular condition and <0.5 in the visual condition (Figure 2F), which was similar to that seen in the real data. On the other hand, if responses of model neurons were decoded with respect to their visual heading preferences, the pattern of CPs across stimulus conditions would reverse and would be incompatible with that observed for MSTd neurons (Figure 2—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)

Predictions from a variant of the selective decoding model in which responses are decoded according to the visual heading tuning of each neuron, instead of the vestibular tuning.This modification reverses the CP patterns across stimulus conditions, as compared to Figure 2E,F, and is inconsistent with experimental data.DOI:http://dx.doi.org/10.7554/eLife.02670.006
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2s2: Predictions from a variant of the selective decoding model in which responses are decoded according to the visual heading tuning of each neuron, instead of the vestibular tuning.This modification reverses the CP patterns across stimulus conditions, as compared to Figure 2E,F, and is inconsistent with experimental data.DOI:http://dx.doi.org/10.7554/eLife.02670.006
Mentions: To examine the predictions of the selective decoding model, we again manipulated two aspects of the readout. With regard to magnitude of the decoding weights, a decoding weight different from zero was essential to produce CPs for opposite cells that were different from the chance level (green symbols/lines vs other colors in Figure 2F). With regard to polarity (or sign) of the decoding weights, decoding responses of opposite cells with respect to their vestibular heading preference led to CP >0.5 in the vestibular condition and <0.5 in the visual condition (Figure 2F), which was similar to that seen in the real data. On the other hand, if responses of model neurons were decoded with respect to their visual heading preferences, the pattern of CPs across stimulus conditions would reverse and would be incompatible with that observed for MSTd neurons (Figure 2—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