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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 “hybrid” model (see text for details) in which correlated noise was assigned according to vestibular signal correlations, and heading was decoded relative to the vestibular heading tuning of each neuron.CP patterns were roughly similar to those seen in the pure-correlation model (Figure 2C,D).DOI:http://dx.doi.org/10.7554/eLife.02670.007
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fig2s3: Predictions from a “hybrid” model (see text for details) in which correlated noise was assigned according to vestibular signal correlations, and heading was decoded relative to the vestibular heading tuning of each neuron.CP patterns were roughly similar to those seen in the pure-correlation model (Figure 2C,D).DOI:http://dx.doi.org/10.7554/eLife.02670.007

Mentions: For completeness, we also considered a hybrid model that combines features from both of the above models. Noise correlations were linearly dependent only on the similarity of vestibular tuning, as in the pure-correlation model. This produced correlations between the two pools (congruent and opposite cells), unlike in the selective decoding model. In addition, a readout weight was assigned to the opposite cells, as in the selective decoding model. Under these conditions, we found that the predicted patterns of CPs largely resembled those from the pure-correlation model, as if the decoding weights on pool 2 played little role (Figure 2—figure supplement 3). In the following analyses, we only considered comparisons between the pure-correlation model and the selective decoding model, as these are conceptually distinct. Although this distinction is useful for exploring the relative roles of correlated noise and selective decoding in producing CPs, we recognize that both factors may contribute.


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 “hybrid” model (see text for details) in which correlated noise was assigned according to vestibular signal correlations, and heading was decoded relative to the vestibular heading tuning of each neuron.CP patterns were roughly similar to those seen in the pure-correlation model (Figure 2C,D).DOI:http://dx.doi.org/10.7554/eLife.02670.007
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2s3: Predictions from a “hybrid” model (see text for details) in which correlated noise was assigned according to vestibular signal correlations, and heading was decoded relative to the vestibular heading tuning of each neuron.CP patterns were roughly similar to those seen in the pure-correlation model (Figure 2C,D).DOI:http://dx.doi.org/10.7554/eLife.02670.007
Mentions: For completeness, we also considered a hybrid model that combines features from both of the above models. Noise correlations were linearly dependent only on the similarity of vestibular tuning, as in the pure-correlation model. This produced correlations between the two pools (congruent and opposite cells), unlike in the selective decoding model. In addition, a readout weight was assigned to the opposite cells, as in the selective decoding model. Under these conditions, we found that the predicted patterns of CPs largely resembled those from the pure-correlation model, as if the decoding weights on pool 2 played little role (Figure 2—figure supplement 3). In the following analyses, we only considered comparisons between the pure-correlation model and the selective decoding model, as these are conceptually distinct. Although this distinction is useful for exploring the relative roles of correlated noise and selective decoding in producing CPs, we recognize that both factors may contribute.

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