Limits...
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.


Related in: MedlinePlus

The GoF indices in the d-prime weighted scheme 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.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4597482&req=5

Figure 8: The GoF indices in the d-prime weighted scheme 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.

Mentions: In terms of the overall fitness quantified as GoF, at extremely short integration time windows (25 ms), the d-prime model failed to reproduce the observed threshold and choice probabilities even when it incorporated a large number of neurons from both the P and M populations (n = 512 P neurons, 512 M neurons; Figure 8A). In 50 ms windows, incorporating a large number of either P or M neurons (n = 256–512) could explain the observed threshold and choice probabilities (Figure 8B). Finally, at medium to long intervals (75–200 ms), a wider range of M/P neuron combinations (n = 32–256) yielded good model performance, but further increasing the number of neurons would result in a decrease in model performance (Figures 8C–F). Comparing Figure 8 (d-prime pooling) with Figure 4 (uniform pooling), it is clear that the temporal evolution of the GoF index for the d-prime model resembled that for the uniform model, but there were apparent differences between the two models in the 50 ms and 75 ms time windows. To be more precise, in the 50 ms window, the d-prime model demonstrated better overall performance than the uniform model (mean difference = 4.85 ± 0.51% GoF, P = 0.00, Wilcoxon signed rank test). In the 75 ms window, in contrast, the overall performance did not differ between the two types of pooling models (mean difference = 0.00 ± 0.48% GoF, P = 0.30, Wilcoxon signed rank test), but the number of neurons needed to achieve good model performance (>90% GoF) was significantly reduced in the d-prime model (d-prime model: n = 64–256 neurons, uniform model: n = 128–512 neurons).


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

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

The GoF indices in the d-prime weighted scheme 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.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4597482&req=5

Figure 8: The GoF indices in the d-prime weighted scheme 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.
Mentions: In terms of the overall fitness quantified as GoF, at extremely short integration time windows (25 ms), the d-prime model failed to reproduce the observed threshold and choice probabilities even when it incorporated a large number of neurons from both the P and M populations (n = 512 P neurons, 512 M neurons; Figure 8A). In 50 ms windows, incorporating a large number of either P or M neurons (n = 256–512) could explain the observed threshold and choice probabilities (Figure 8B). Finally, at medium to long intervals (75–200 ms), a wider range of M/P neuron combinations (n = 32–256) yielded good model performance, but further increasing the number of neurons would result in a decrease in model performance (Figures 8C–F). Comparing Figure 8 (d-prime pooling) with Figure 4 (uniform pooling), it is clear that the temporal evolution of the GoF index for the d-prime model resembled that for the uniform model, but there were apparent differences between the two models in the 50 ms and 75 ms time windows. To be more precise, in the 50 ms window, the d-prime model demonstrated better overall performance than the uniform model (mean difference = 4.85 ± 0.51% GoF, P = 0.00, Wilcoxon signed rank test). In the 75 ms window, in contrast, the overall performance did not differ between the two types of pooling models (mean difference = 0.00 ± 0.48% GoF, P = 0.30, Wilcoxon signed rank test), but the number of neurons needed to achieve good model performance (>90% GoF) was significantly reduced in the d-prime model (d-prime model: n = 64–256 neurons, uniform model: n = 128–512 neurons).

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