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A computational relationship between thalamic sensory neural responses and contrast perception.

Jiang Y, Purushothaman G, Casagrande VA - Front Neural Circuits (2015)

Bottom Line: We have now computationally tested a number of specific hypotheses relating these measured LGN neural responses to the contrast detection behavior of the animals.We modeled the perceptual decisions with different numbers of neurons and using a variety of pooling/readout strategies, and found that the most successful model consisted of about 50-200 LGN neurons, with individual neurons weighted differentially according to their signal-to-noise ratios (quantified as d-primes).These results supported the hypothesis that in contrast detection the perceptual decision pool consists of multiple thalamic neurons, and that the response fluctuations in these neurons can influence contrast perception, with the more sensitive thalamic neurons likely to exert a greater influence.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Vanderbilt University Nashville, TN, USA.

ABSTRACT
Uncovering the relationship between sensory neural responses and perceptual decisions remains a fundamental problem in neuroscience. Decades of experimental and modeling work in the sensory cortex have demonstrated that a perceptual decision pool is usually composed of tens to hundreds of neurons, the responses of which are significantly correlated not only with each other, but also with the behavioral choices of an animal. Few studies, however, have measured neural activity in the sensory thalamus of awake, behaving animals. Therefore, it remains unclear how many thalamic neurons are recruited and how the information from these neurons is pooled at subsequent cortical stages to form a perceptual decision. In a previous study we measured neural activity in the macaque lateral geniculate nucleus (LGN) during a two alternative forced choice (2AFC) contrast detection task, and found that single LGN neurons were significantly correlated with the monkeys' behavioral choices, despite their relatively poor contrast sensitivity and a lack of overall interneuronal correlations. We have now computationally tested a number of specific hypotheses relating these measured LGN neural responses to the contrast detection behavior of the animals. We modeled the perceptual decisions with different numbers of neurons and using a variety of pooling/readout strategies, and found that the most successful model consisted of about 50-200 LGN neurons, with individual neurons weighted differentially according to their signal-to-noise ratios (quantified as d-primes). These results supported the hypothesis that in contrast detection the perceptual decision pool consists of multiple thalamic neurons, and that the response fluctuations in these neurons can influence contrast perception, with the more sensitive thalamic neurons likely to exert a greater influence.

No MeSH data available.


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The Goodness-of-Fit (GoF) indices for different pool sizes (n) and integration time windows (t). A GoF of 100% (white) indicates that the model perfectly matches the observed psychometric threshold as well as choice probabilities for both P and M neurons. (A)t = 0–25 ms. (B)t = 0–50 ms. (C)t = 0–75 ms. (D)t = 0–100 ms. (E)t = 0–150 ms. (F)t = 0–200 ms. (A,B) and (D–F) were adapted from Jiang et al. (2015).
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Figure 4: The Goodness-of-Fit (GoF) indices for different pool sizes (n) and integration time windows (t). A GoF of 100% (white) indicates that the model perfectly matches the observed psychometric threshold as well as choice probabilities for both P and M neurons. (A)t = 0–25 ms. (B)t = 0–50 ms. (C)t = 0–75 ms. (D)t = 0–100 ms. (E)t = 0–150 ms. (F)t = 0–200 ms. (A,B) and (D–F) were adapted from Jiang et al. (2015).

Mentions: To evaluate the overall performance of this model, a Goodness-of-Fit (GoF) index (see equation [2]) was reported for each (n, t) parameter combination. A GoF (ranging from 0–100%) reflected three factors equally: (1) how close the simulated “psychophysical” threshold approached the measured psychophysical threshold; (2) how close the simulated P population choice probability approached the measured average choice probability for P neurons; and (3) how close the simulated M population choice probability approached the measured average choice probability for M neurons. A GoF of 100% indicated that our simulation perfectly matched the observed psychometric threshold and the choice probabilities for both types of neurons. By changing the duration of the integration window (25–200 ms), we found that: (1) at extremely short intervals (25 ms), even incorporating a large number of neurons from both groups (n = 512 P neurons, 512 M neurons) still failed to reproduce the observed threshold and choice probabilities (Figure 4A); (2) in 50 ms, preferably incorporating a large number of M neurons (n = 256–512) could explain the observed threshold and choice probabilities (Figure 4B); (3) in 75 ms, incorporating a large number of either P or M neurons (n = 128–512) could achieve good overall model performance (Figure 4C); and (4) at medium to long intervals (100–200 ms), a smaller number of P and M neurons were needed (n = 32–128) to achieve good model performance, but further increasing the number of neurons resulted in a decrease in model performance (Figures 4D–F).


A computational relationship between thalamic sensory neural responses and contrast perception.

Jiang Y, Purushothaman G, Casagrande VA - Front Neural Circuits (2015)

The Goodness-of-Fit (GoF) indices for different pool sizes (n) and integration time windows (t). A GoF of 100% (white) indicates that the model perfectly matches the observed psychometric threshold as well as choice probabilities for both P and M neurons. (A)t = 0–25 ms. (B)t = 0–50 ms. (C)t = 0–75 ms. (D)t = 0–100 ms. (E)t = 0–150 ms. (F)t = 0–200 ms. (A,B) and (D–F) were adapted from Jiang et al. (2015).
© Copyright Policy
Related In: Results  -  Collection

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Figure 4: The Goodness-of-Fit (GoF) indices for different pool sizes (n) and integration time windows (t). A GoF of 100% (white) indicates that the model perfectly matches the observed psychometric threshold as well as choice probabilities for both P and M neurons. (A)t = 0–25 ms. (B)t = 0–50 ms. (C)t = 0–75 ms. (D)t = 0–100 ms. (E)t = 0–150 ms. (F)t = 0–200 ms. (A,B) and (D–F) were adapted from Jiang et al. (2015).
Mentions: To evaluate the overall performance of this model, a Goodness-of-Fit (GoF) index (see equation [2]) was reported for each (n, t) parameter combination. A GoF (ranging from 0–100%) reflected three factors equally: (1) how close the simulated “psychophysical” threshold approached the measured psychophysical threshold; (2) how close the simulated P population choice probability approached the measured average choice probability for P neurons; and (3) how close the simulated M population choice probability approached the measured average choice probability for M neurons. A GoF of 100% indicated that our simulation perfectly matched the observed psychometric threshold and the choice probabilities for both types of neurons. By changing the duration of the integration window (25–200 ms), we found that: (1) at extremely short intervals (25 ms), even incorporating a large number of neurons from both groups (n = 512 P neurons, 512 M neurons) still failed to reproduce the observed threshold and choice probabilities (Figure 4A); (2) in 50 ms, preferably incorporating a large number of M neurons (n = 256–512) could explain the observed threshold and choice probabilities (Figure 4B); (3) in 75 ms, incorporating a large number of either P or M neurons (n = 128–512) could achieve good overall model performance (Figure 4C); and (4) at medium to long intervals (100–200 ms), a smaller number of P and M neurons were needed (n = 32–128) to achieve good model performance, but further increasing the number of neurons resulted in a decrease in model performance (Figures 4D–F).

Bottom Line: We have now computationally tested a number of specific hypotheses relating these measured LGN neural responses to the contrast detection behavior of the animals.We modeled the perceptual decisions with different numbers of neurons and using a variety of pooling/readout strategies, and found that the most successful model consisted of about 50-200 LGN neurons, with individual neurons weighted differentially according to their signal-to-noise ratios (quantified as d-primes).These results supported the hypothesis that in contrast detection the perceptual decision pool consists of multiple thalamic neurons, and that the response fluctuations in these neurons can influence contrast perception, with the more sensitive thalamic neurons likely to exert a greater influence.

View Article: PubMed Central - PubMed

Affiliation: Department of Psychology, Vanderbilt University Nashville, TN, USA.

ABSTRACT
Uncovering the relationship between sensory neural responses and perceptual decisions remains a fundamental problem in neuroscience. Decades of experimental and modeling work in the sensory cortex have demonstrated that a perceptual decision pool is usually composed of tens to hundreds of neurons, the responses of which are significantly correlated not only with each other, but also with the behavioral choices of an animal. Few studies, however, have measured neural activity in the sensory thalamus of awake, behaving animals. Therefore, it remains unclear how many thalamic neurons are recruited and how the information from these neurons is pooled at subsequent cortical stages to form a perceptual decision. In a previous study we measured neural activity in the macaque lateral geniculate nucleus (LGN) during a two alternative forced choice (2AFC) contrast detection task, and found that single LGN neurons were significantly correlated with the monkeys' behavioral choices, despite their relatively poor contrast sensitivity and a lack of overall interneuronal correlations. We have now computationally tested a number of specific hypotheses relating these measured LGN neural responses to the contrast detection behavior of the animals. We modeled the perceptual decisions with different numbers of neurons and using a variety of pooling/readout strategies, and found that the most successful model consisted of about 50-200 LGN neurons, with individual neurons weighted differentially according to their signal-to-noise ratios (quantified as d-primes). These results supported the hypothesis that in contrast detection the perceptual decision pool consists of multiple thalamic neurons, and that the response fluctuations in these neurons can influence contrast perception, with the more sensitive thalamic neurons likely to exert a greater influence.

No MeSH data available.


Related in: MedlinePlus