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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.


Related in: MedlinePlus

Further analysis of the d-prime weighted pooling scheme: P and M neurons weighted together in reference to one maximal d-prime. (A) Cumulative d-prime distributions for P (magenta) and M (green) neurons in a 0–150 ms window (n = 512 neurons, 200 simulations). (B) D-prime distributions for P (magenta) and M (green) neurons in a 0–150 ms window (n = 512 neurons, 200 simulations). Arrow: median d-prime; solid line: d-prime = 0. (C) Cumulative weight distributions for P (magenta) and M (green) neurons in the same 0–150 ms window (n = 512 neurons, 200 simulations). (D) Weight distributions for P (magenta) and M (green) neurons in the same 0–150 ms window (n = 512 neurons, 200 simulations). Arrow: median weight; solid line: weight = 0. (E) Cumulative choice probability distributions for P (magenta) and M (green) neurons in the same 0–150 ms window (n = 512 neurons, 200 simulations). (F) Cumulative choice probability distributions for P (magenta) and M (green) neurons in different integration time windows (n = 512 neurons).
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Figure 10: Further analysis of the d-prime weighted pooling scheme: P and M neurons weighted together in reference to one maximal d-prime. (A) Cumulative d-prime distributions for P (magenta) and M (green) neurons in a 0–150 ms window (n = 512 neurons, 200 simulations). (B) D-prime distributions for P (magenta) and M (green) neurons in a 0–150 ms window (n = 512 neurons, 200 simulations). Arrow: median d-prime; solid line: d-prime = 0. (C) Cumulative weight distributions for P (magenta) and M (green) neurons in the same 0–150 ms window (n = 512 neurons, 200 simulations). (D) Weight distributions for P (magenta) and M (green) neurons in the same 0–150 ms window (n = 512 neurons, 200 simulations). Arrow: median weight; solid line: weight = 0. (E) Cumulative choice probability distributions for P (magenta) and M (green) neurons in the same 0–150 ms window (n = 512 neurons, 200 simulations). (F) Cumulative choice probability distributions for P (magenta) and M (green) neurons in different integration time windows (n = 512 neurons).

Mentions: First, as the d-prime value is a direct reflection of the signal-to-noise ratio of single neural responses, it is not surprising that the d-prime distributions remained the same regardless of the pooling strategy (compare Figures 9C,D to 10A,B). Specifically, for the P population, the average d-prime here was 1.70 ± 0.00 and the median was 1.40. For the M population, the average d-prime here was 1.70 ± 0.00 and the median was 1.70 as well. Additionally, the P and M d-prime distributions differed significantly in their shapes, with the P d-prime distribution much more widely spread (P interquartile range = 2.04, M interquartile range = 1.25) and positively skewed (P skewness index = 0.73, M skewness index = 0.11; Figures 10A,B). The weight distributions for P and M neurons in the same time window (t = 0–150 ms), however, were very different between the two types of d-prime models (compare Figures 9E,F to 10C,D). Specifically, when P and M neurons were pooled together, as was the case here, the weight distributions were still scaled-down versions of their corresponding d-prime distributions (P weight: mean = 0.29 ± 0.00, median = 0.24; M weight: mean = 0.29 ± 0.00, median = 0.29), but both distributions retained their shape and spread. In other words, the weight distributions for P and M neurons still differed from each other in terms of both spread (P interquartile range = 0.36, M interquartile range = 0.21) and skewness (P skewness index = 0.74, M skewness index = 0.14; Figures 10C,D). Two-way ANOVAs confirmed that while the d-prime distributions did not differ (F = 0.01, P = 0.91, 2-way ANOVA main effect for pooling strategy), the weight distributions differed dramatically between the two pooling schemes (F = 12073.18, P = 0.00, 2-way ANOVA main effect for pooling strategy). This difference in the weight distributions was presumably due to the fact that, compared with M d-prime distributions, P d-prime distributions were more widely spread with greater maximal values. Thus, when P and M populations were scaled together, as was the case here, both were most likely scaled in reference to the d-primes of a subset of P neurons, thus preserving the shapes as well as spreads of these distributions. When P and M populations were scaled separately, as was the case above, M neurons were scaled to a lesser degree when compared with P neurons, rendering the spreads of the two distributions indistinguishable.


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

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

Further analysis of the d-prime weighted pooling scheme: P and M neurons weighted together in reference to one maximal d-prime. (A) Cumulative d-prime distributions for P (magenta) and M (green) neurons in a 0–150 ms window (n = 512 neurons, 200 simulations). (B) D-prime distributions for P (magenta) and M (green) neurons in a 0–150 ms window (n = 512 neurons, 200 simulations). Arrow: median d-prime; solid line: d-prime = 0. (C) Cumulative weight distributions for P (magenta) and M (green) neurons in the same 0–150 ms window (n = 512 neurons, 200 simulations). (D) Weight distributions for P (magenta) and M (green) neurons in the same 0–150 ms window (n = 512 neurons, 200 simulations). Arrow: median weight; solid line: weight = 0. (E) Cumulative choice probability distributions for P (magenta) and M (green) neurons in the same 0–150 ms window (n = 512 neurons, 200 simulations). (F) Cumulative choice probability distributions for P (magenta) and M (green) neurons in different integration time windows (n = 512 neurons).
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Figure 10: Further analysis of the d-prime weighted pooling scheme: P and M neurons weighted together in reference to one maximal d-prime. (A) Cumulative d-prime distributions for P (magenta) and M (green) neurons in a 0–150 ms window (n = 512 neurons, 200 simulations). (B) D-prime distributions for P (magenta) and M (green) neurons in a 0–150 ms window (n = 512 neurons, 200 simulations). Arrow: median d-prime; solid line: d-prime = 0. (C) Cumulative weight distributions for P (magenta) and M (green) neurons in the same 0–150 ms window (n = 512 neurons, 200 simulations). (D) Weight distributions for P (magenta) and M (green) neurons in the same 0–150 ms window (n = 512 neurons, 200 simulations). Arrow: median weight; solid line: weight = 0. (E) Cumulative choice probability distributions for P (magenta) and M (green) neurons in the same 0–150 ms window (n = 512 neurons, 200 simulations). (F) Cumulative choice probability distributions for P (magenta) and M (green) neurons in different integration time windows (n = 512 neurons).
Mentions: First, as the d-prime value is a direct reflection of the signal-to-noise ratio of single neural responses, it is not surprising that the d-prime distributions remained the same regardless of the pooling strategy (compare Figures 9C,D to 10A,B). Specifically, for the P population, the average d-prime here was 1.70 ± 0.00 and the median was 1.40. For the M population, the average d-prime here was 1.70 ± 0.00 and the median was 1.70 as well. Additionally, the P and M d-prime distributions differed significantly in their shapes, with the P d-prime distribution much more widely spread (P interquartile range = 2.04, M interquartile range = 1.25) and positively skewed (P skewness index = 0.73, M skewness index = 0.11; Figures 10A,B). The weight distributions for P and M neurons in the same time window (t = 0–150 ms), however, were very different between the two types of d-prime models (compare Figures 9E,F to 10C,D). Specifically, when P and M neurons were pooled together, as was the case here, the weight distributions were still scaled-down versions of their corresponding d-prime distributions (P weight: mean = 0.29 ± 0.00, median = 0.24; M weight: mean = 0.29 ± 0.00, median = 0.29), but both distributions retained their shape and spread. In other words, the weight distributions for P and M neurons still differed from each other in terms of both spread (P interquartile range = 0.36, M interquartile range = 0.21) and skewness (P skewness index = 0.74, M skewness index = 0.14; Figures 10C,D). Two-way ANOVAs confirmed that while the d-prime distributions did not differ (F = 0.01, P = 0.91, 2-way ANOVA main effect for pooling strategy), the weight distributions differed dramatically between the two pooling schemes (F = 12073.18, P = 0.00, 2-way ANOVA main effect for pooling strategy). This difference in the weight distributions was presumably due to the fact that, compared with M d-prime distributions, P d-prime distributions were more widely spread with greater maximal values. Thus, when P and M populations were scaled together, as was the case here, both were most likely scaled in reference to the d-primes of a subset of P neurons, thus preserving the shapes as well as spreads of these distributions. When P and M populations were scaled separately, as was the case above, M neurons were scaled to a lesser degree when compared with P neurons, rendering the spreads of the two distributions indistinguishable.

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