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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.

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A comparison of threshold and choice probability values derived from different pooling schemes. (A) The average psychometric threshold decreased with time in all pooling schemes (n = 1–512 neurons). Black: uniform pooling (as above); gray: every neuron weighted by its response amplitude in every trial; blue: every neuron weighted by its average response amplitude at high contrast (80–99%); magenta: every neuron weighted by its d-prime value at high contrast (80–99%); error bar: mean ± SEM. (B) Different pooling schemes yielded significantly different psychometric thresholds when compared with the uniform pooling model (n = 1–512 neurons). Gray: every neuron weighted by its response amplitude in every trial; blue with circle: every neuron weighted by its average response amplitude at high contrast (80–99%), with P and M neurons weighted separately in reference to their respective maximal responses; blue with triangle: every neuron weighted by its average response amplitude at high contrast (80–99%), with P and M neurons weighted together in reference to one maximal response; magenta with circle: every neuron weighted by its d-prime value at high contrast (80–99%), with P and M neurons weighted separately in reference to their respective maximal d-primes; magenta with triangle: every neuron weighted by its d-prime value at high contrast (80–99%), with P and M neurons weighted together in reference to one maximal d-prime; y axis: the difference in psychometric threshold (alternative pooling scheme—uniform, % contrast); error bar: mean ± SEM. (C) The minimal psychometric threshold achieved by the model decreased with time in all pooling schemes. (D,E) Cumulative choice probability distributions for P (D) and M (E) neurons (n = 512 neurons, t = 0–150 ms) in different pooling schemes. (F–H) Cumulative choice probability distributions for P (magenta) and M (green) neurons in different integration time windows (n = 512 neurons), with every neuron weighted by its response amplitude in every trial (F), by its average response amplitude (G), or by its d-prime (H).
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Figure 5: A comparison of threshold and choice probability values derived from different pooling schemes. (A) The average psychometric threshold decreased with time in all pooling schemes (n = 1–512 neurons). Black: uniform pooling (as above); gray: every neuron weighted by its response amplitude in every trial; blue: every neuron weighted by its average response amplitude at high contrast (80–99%); magenta: every neuron weighted by its d-prime value at high contrast (80–99%); error bar: mean ± SEM. (B) Different pooling schemes yielded significantly different psychometric thresholds when compared with the uniform pooling model (n = 1–512 neurons). Gray: every neuron weighted by its response amplitude in every trial; blue with circle: every neuron weighted by its average response amplitude at high contrast (80–99%), with P and M neurons weighted separately in reference to their respective maximal responses; blue with triangle: every neuron weighted by its average response amplitude at high contrast (80–99%), with P and M neurons weighted together in reference to one maximal response; magenta with circle: every neuron weighted by its d-prime value at high contrast (80–99%), with P and M neurons weighted separately in reference to their respective maximal d-primes; magenta with triangle: every neuron weighted by its d-prime value at high contrast (80–99%), with P and M neurons weighted together in reference to one maximal d-prime; y axis: the difference in psychometric threshold (alternative pooling scheme—uniform, % contrast); error bar: mean ± SEM. (C) The minimal psychometric threshold achieved by the model decreased with time in all pooling schemes. (D,E) Cumulative choice probability distributions for P (D) and M (E) neurons (n = 512 neurons, t = 0–150 ms) in different pooling schemes. (F–H) Cumulative choice probability distributions for P (magenta) and M (green) neurons in different integration time windows (n = 512 neurons), with every neuron weighted by its response amplitude in every trial (F), by its average response amplitude (G), or by its d-prime (H).

Mentions: In this section we examine several alternative pooling schemes where, instead of assigning the same weight to all neurons, each individual neuron was weighted differentially based on its response rate or sensitivity. We investigated three main categories of weighted pooling schemes, namely the amplitude-per-trial (amp/trial) weighted, the mean amplitude (mean amp) weighted, and the d-prime weighted schemes (see “Material and Methods” Section). First we compared the simulated psychometric thresholds and found that for all pooling schemes the average psychometric threshold decreased with time (n = 1–512 neurons, F = 920.22, P = 0.00, 2-way ANOVA main effect for time). Furthermore, there was a significant difference in psychometric thresholds among different pooling schemes (F = 54.82, P = 0.00, 2-way ANOVA main effect for pooling strategy), and this difference changed across time (F = 8.43, P = 0.00, 2-way ANOVA interaction effect; Figure 5A). Next we examined whether these alternative pooling strategies improved the sensitivity of the model when compared to the uniform pooling strategy. Here the mean amplitude weighted and the d-prime weighted models could be further divided into two subcategories, respectively, depending on whether P and M neurons were weighted separately or together in the model. Among all of these alternative pooling schemes, we found that only the d-prime weighted schemes consistently improved the psychophysical performance when compared with the uniform pooling scheme (mean difference, d-prime 1 = −4.88 ± 0.42% contrast; mean difference, d-prime 2 = −4.67 ± 0.43% contrast; P < 0.05, Tukey’s HSD tests for multiple comparisons). The mean amplitude weighted schemes and the amplitude per trial weighted scheme all failed to perform as well as the uniform pooling scheme in terms of the threshold (mean difference, mean amp 1 = 2.59 ± 0.21% contrast; mean difference, mean amp 2 = 3.31 ± 0.23% contrast; mean difference, amp/trial = 7.51 ± 0.28% contrast; P < 0.05, Tukey’s HSD tests for multiple comparisons). Additionally, the two subtypes of mean amplitude weighted models did not differ from each other in terms of their simulated thresholds, and the two subtypes of d-prime weighted models did not differ from each other either (P > 0.05, Tukey’s HSD tests for multiple comparisons; Figure 5B). Finally, the minimal psychophysical threshold achieved by the model also decreased with time in all pooling schemes and plateaued at around 50–75 ms after stimulus onset (n = 1–512 neurons, minimal threshold = 2–3% contrast; Figure 5C). Taken together, Figures 5A–C demonstrated that the d-prime weighted pooling strategies were the most optimal in terms of the simulated psychophysical performance, and this advantage over other pooling strategies was the most apparent in short integration time windows (25–50 ms).


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

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

A comparison of threshold and choice probability values derived from different pooling schemes. (A) The average psychometric threshold decreased with time in all pooling schemes (n = 1–512 neurons). Black: uniform pooling (as above); gray: every neuron weighted by its response amplitude in every trial; blue: every neuron weighted by its average response amplitude at high contrast (80–99%); magenta: every neuron weighted by its d-prime value at high contrast (80–99%); error bar: mean ± SEM. (B) Different pooling schemes yielded significantly different psychometric thresholds when compared with the uniform pooling model (n = 1–512 neurons). Gray: every neuron weighted by its response amplitude in every trial; blue with circle: every neuron weighted by its average response amplitude at high contrast (80–99%), with P and M neurons weighted separately in reference to their respective maximal responses; blue with triangle: every neuron weighted by its average response amplitude at high contrast (80–99%), with P and M neurons weighted together in reference to one maximal response; magenta with circle: every neuron weighted by its d-prime value at high contrast (80–99%), with P and M neurons weighted separately in reference to their respective maximal d-primes; magenta with triangle: every neuron weighted by its d-prime value at high contrast (80–99%), with P and M neurons weighted together in reference to one maximal d-prime; y axis: the difference in psychometric threshold (alternative pooling scheme—uniform, % contrast); error bar: mean ± SEM. (C) The minimal psychometric threshold achieved by the model decreased with time in all pooling schemes. (D,E) Cumulative choice probability distributions for P (D) and M (E) neurons (n = 512 neurons, t = 0–150 ms) in different pooling schemes. (F–H) Cumulative choice probability distributions for P (magenta) and M (green) neurons in different integration time windows (n = 512 neurons), with every neuron weighted by its response amplitude in every trial (F), by its average response amplitude (G), or by its d-prime (H).
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Figure 5: A comparison of threshold and choice probability values derived from different pooling schemes. (A) The average psychometric threshold decreased with time in all pooling schemes (n = 1–512 neurons). Black: uniform pooling (as above); gray: every neuron weighted by its response amplitude in every trial; blue: every neuron weighted by its average response amplitude at high contrast (80–99%); magenta: every neuron weighted by its d-prime value at high contrast (80–99%); error bar: mean ± SEM. (B) Different pooling schemes yielded significantly different psychometric thresholds when compared with the uniform pooling model (n = 1–512 neurons). Gray: every neuron weighted by its response amplitude in every trial; blue with circle: every neuron weighted by its average response amplitude at high contrast (80–99%), with P and M neurons weighted separately in reference to their respective maximal responses; blue with triangle: every neuron weighted by its average response amplitude at high contrast (80–99%), with P and M neurons weighted together in reference to one maximal response; magenta with circle: every neuron weighted by its d-prime value at high contrast (80–99%), with P and M neurons weighted separately in reference to their respective maximal d-primes; magenta with triangle: every neuron weighted by its d-prime value at high contrast (80–99%), with P and M neurons weighted together in reference to one maximal d-prime; y axis: the difference in psychometric threshold (alternative pooling scheme—uniform, % contrast); error bar: mean ± SEM. (C) The minimal psychometric threshold achieved by the model decreased with time in all pooling schemes. (D,E) Cumulative choice probability distributions for P (D) and M (E) neurons (n = 512 neurons, t = 0–150 ms) in different pooling schemes. (F–H) Cumulative choice probability distributions for P (magenta) and M (green) neurons in different integration time windows (n = 512 neurons), with every neuron weighted by its response amplitude in every trial (F), by its average response amplitude (G), or by its d-prime (H).
Mentions: In this section we examine several alternative pooling schemes where, instead of assigning the same weight to all neurons, each individual neuron was weighted differentially based on its response rate or sensitivity. We investigated three main categories of weighted pooling schemes, namely the amplitude-per-trial (amp/trial) weighted, the mean amplitude (mean amp) weighted, and the d-prime weighted schemes (see “Material and Methods” Section). First we compared the simulated psychometric thresholds and found that for all pooling schemes the average psychometric threshold decreased with time (n = 1–512 neurons, F = 920.22, P = 0.00, 2-way ANOVA main effect for time). Furthermore, there was a significant difference in psychometric thresholds among different pooling schemes (F = 54.82, P = 0.00, 2-way ANOVA main effect for pooling strategy), and this difference changed across time (F = 8.43, P = 0.00, 2-way ANOVA interaction effect; Figure 5A). Next we examined whether these alternative pooling strategies improved the sensitivity of the model when compared to the uniform pooling strategy. Here the mean amplitude weighted and the d-prime weighted models could be further divided into two subcategories, respectively, depending on whether P and M neurons were weighted separately or together in the model. Among all of these alternative pooling schemes, we found that only the d-prime weighted schemes consistently improved the psychophysical performance when compared with the uniform pooling scheme (mean difference, d-prime 1 = −4.88 ± 0.42% contrast; mean difference, d-prime 2 = −4.67 ± 0.43% contrast; P < 0.05, Tukey’s HSD tests for multiple comparisons). The mean amplitude weighted schemes and the amplitude per trial weighted scheme all failed to perform as well as the uniform pooling scheme in terms of the threshold (mean difference, mean amp 1 = 2.59 ± 0.21% contrast; mean difference, mean amp 2 = 3.31 ± 0.23% contrast; mean difference, amp/trial = 7.51 ± 0.28% contrast; P < 0.05, Tukey’s HSD tests for multiple comparisons). Additionally, the two subtypes of mean amplitude weighted models did not differ from each other in terms of their simulated thresholds, and the two subtypes of d-prime weighted models did not differ from each other either (P > 0.05, Tukey’s HSD tests for multiple comparisons; Figure 5B). Finally, the minimal psychophysical threshold achieved by the model also decreased with time in all pooling schemes and plateaued at around 50–75 ms after stimulus onset (n = 1–512 neurons, minimal threshold = 2–3% contrast; Figure 5C). Taken together, Figures 5A–C demonstrated that the d-prime weighted pooling strategies were the most optimal in terms of the simulated psychophysical performance, and this advantage over other pooling strategies was the most apparent in short integration time windows (25–50 ms).

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