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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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Comparison of models in which choice probabilities (CPs) arise through either correlated noise or selective decoding.Each model consists of two pools of neurons (500 neurons each) with equal numbers of neurons that prefer leftward and rightward headings. In the 'pure-correlation' model (A and B), neurons in pool 2 make no contribution to the decision and activity within or across pools is correlated according to the relationship illustrated in panel B. In the 'selective decoding' model (C and D), neurons shared correlated noise within each pool but not across pools. Neurons in pool 1 were always given a decoding weight of 1, while neurons in pool 2 were given weights ranging from 0 to 1. Solid curves in D: responses of pool 2 were decoded according to each neuron's preferred stimulus; dashed curves: pool 2 responses were decoded relative to each neuron's anti-preferred stimulus. Dashed black horizontal line: CP = 0.5.DOI:http://dx.doi.org/10.7554/eLife.02670.003
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fig1: Comparison of models in which choice probabilities (CPs) arise through either correlated noise or selective decoding.Each model consists of two pools of neurons (500 neurons each) with equal numbers of neurons that prefer leftward and rightward headings. In the 'pure-correlation' model (A and B), neurons in pool 2 make no contribution to the decision and activity within or across pools is correlated according to the relationship illustrated in panel B. In the 'selective decoding' model (C and D), neurons shared correlated noise within each pool but not across pools. Neurons in pool 1 were always given a decoding weight of 1, while neurons in pool 2 were given weights ranging from 0 to 1. Solid curves in D: responses of pool 2 were decoded according to each neuron's preferred stimulus; dashed curves: pool 2 responses were decoded relative to each neuron's anti-preferred stimulus. Dashed black horizontal line: CP = 0.5.DOI:http://dx.doi.org/10.7554/eLife.02670.003

Mentions: As a prelude to considering the multisensory situation, we consider two extreme cases in which CPs of a group of neurons are driven mainly by correlated noise or by selective decoding. In both schemes, structured noise correlations are necessary to observe significant CPs, but the models differ in terms of which pools of neurons are correlated and how they are decoded. For the ‘pure-correlation’ model (Figure 1A,B), only correlated noise is needed to produce CPs that are significantly different from the chance level of 0.5. In this model, we divided the population of neurons into two groups, each of which contained an equal number of neurons preferring leftward and rightward headings. The first group of neurons (pool 1 in Figure 1A) contributed to the decoder's heading report (decoding weight = 1), while responses from the other group (pool 2) were ignored by the decoder (decoding weight = 0). We then examined the CPs of pool 2 neurons as a function of their correlations with pool 1. Although the signals from pool 2 neurons did not contribute to the decoder output, they still exhibited significant CPs as long as their noise was correlated with that of pool 1 neurons (Cohen and Newsome, 2009).10.7554/eLife.02670.003Figure 1.Comparison of models in which choice probabilities (CPs) arise through either correlated noise or selective decoding.


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 models in which choice probabilities (CPs) arise through either correlated noise or selective decoding.Each model consists of two pools of neurons (500 neurons each) with equal numbers of neurons that prefer leftward and rightward headings. In the 'pure-correlation' model (A and B), neurons in pool 2 make no contribution to the decision and activity within or across pools is correlated according to the relationship illustrated in panel B. In the 'selective decoding' model (C and D), neurons shared correlated noise within each pool but not across pools. Neurons in pool 1 were always given a decoding weight of 1, while neurons in pool 2 were given weights ranging from 0 to 1. Solid curves in D: responses of pool 2 were decoded according to each neuron's preferred stimulus; dashed curves: pool 2 responses were decoded relative to each neuron's anti-preferred stimulus. Dashed black horizontal line: CP = 0.5.DOI:http://dx.doi.org/10.7554/eLife.02670.003
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4109308&req=5

fig1: Comparison of models in which choice probabilities (CPs) arise through either correlated noise or selective decoding.Each model consists of two pools of neurons (500 neurons each) with equal numbers of neurons that prefer leftward and rightward headings. In the 'pure-correlation' model (A and B), neurons in pool 2 make no contribution to the decision and activity within or across pools is correlated according to the relationship illustrated in panel B. In the 'selective decoding' model (C and D), neurons shared correlated noise within each pool but not across pools. Neurons in pool 1 were always given a decoding weight of 1, while neurons in pool 2 were given weights ranging from 0 to 1. Solid curves in D: responses of pool 2 were decoded according to each neuron's preferred stimulus; dashed curves: pool 2 responses were decoded relative to each neuron's anti-preferred stimulus. Dashed black horizontal line: CP = 0.5.DOI:http://dx.doi.org/10.7554/eLife.02670.003
Mentions: As a prelude to considering the multisensory situation, we consider two extreme cases in which CPs of a group of neurons are driven mainly by correlated noise or by selective decoding. In both schemes, structured noise correlations are necessary to observe significant CPs, but the models differ in terms of which pools of neurons are correlated and how they are decoded. For the ‘pure-correlation’ model (Figure 1A,B), only correlated noise is needed to produce CPs that are significantly different from the chance level of 0.5. In this model, we divided the population of neurons into two groups, each of which contained an equal number of neurons preferring leftward and rightward headings. The first group of neurons (pool 1 in Figure 1A) contributed to the decoder's heading report (decoding weight = 1), while responses from the other group (pool 2) were ignored by the decoder (decoding weight = 0). We then examined the CPs of pool 2 neurons as a function of their correlations with pool 1. Although the signals from pool 2 neurons did not contribute to the decoder output, they still exhibited significant CPs as long as their noise was correlated with that of pool 1 neurons (Cohen and Newsome, 2009).10.7554/eLife.02670.003Figure 1.Comparison of models in which choice probabilities (CPs) arise through either correlated noise or selective decoding.

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