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a, Schematic representation of the behavioral paradigm designed to evaluate olfactory and tactospatial WM (OWM and TSWM, respectively) in head-restrained mice. For each trial, both sample and target cues were presented for one second (see main text and methods for further details). b, Learning curves for OWM and TSWM tasks (fixed intercue delay of 5 and 1s, respectively; the number of mice is indicated; one-way repeated measures ANOVA). c, Change in performance between the first and the last 100 trials of the training phase shown in b for each mouse (black lines; paired t-test) and the mean population performance (colored bars). d, WM performances plotted as a function of the intercue delay duration (2WRM ANOVA, post-hoc Fisher’s test *: P < 0.05). e, Variability of WM performance across all individual animals shown for the D-prime parameter computed during the OWM task. Examples of <t>exponential</t> fit (used to calculate the amplitude at 0 delay and the half-life) of a few individual performance curves are illustrated (red dashed lines). f-h, Comparison of WM performance amplitude at 0 delay and half-life for OWM and TSWM tasks (unpaired t-test). i, Cumulative probability distribution of half-lives for OWM and TSWM tasks (Kolmogorov- Smirnov test). Data in b , d , f-h are presented as mean ± SEM. See Supplementary Table 1 for detailed statistics.
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a, Schematic representation of the behavioral paradigm designed to evaluate olfactory and tactospatial WM (OWM and TSWM, respectively) in head-restrained mice. For each trial, both sample and target cues were presented for one second (see main text and methods for further details). b, Learning curves for OWM and TSWM tasks (fixed intercue delay of 5 and 1s, respectively; the number of mice is indicated; one-way repeated measures ANOVA). c, Change in performance between the first and the last 100 trials of the training phase shown in b for each mouse (black lines; paired t-test) and the mean population performance (colored bars). d, WM performances plotted as a function of the intercue delay duration (2WRM ANOVA, post-hoc Fisher’s test *: P < 0.05). e, Variability of WM performance across all individual animals shown for the D-prime parameter computed during the OWM task. Examples of <t>exponential</t> fit (used to calculate the amplitude at 0 delay and the half-life) of a few individual performance curves are illustrated (red dashed lines). f-h, Comparison of WM performance amplitude at 0 delay and half-life for OWM and TSWM tasks (unpaired t-test). i, Cumulative probability distribution of half-lives for OWM and TSWM tasks (Kolmogorov- Smirnov test). Data in b , d , f-h are presented as mean ± SEM. See Supplementary Table 1 for detailed statistics.
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a, Schematic representation of the behavioral paradigm designed to evaluate olfactory and tactospatial WM (OWM and TSWM, respectively) in head-restrained mice. For each trial, both sample and target cues were presented for one second (see main text and methods for further details). b, Learning curves for OWM and TSWM tasks (fixed intercue delay of 5 and 1s, respectively; the number of mice is indicated; one-way repeated measures ANOVA). c, Change in performance between the first and the last 100 trials of the training phase shown in b for each mouse (black lines; paired t-test) and the mean population performance (colored bars). d, WM performances plotted as a function of the intercue delay duration (2WRM ANOVA, post-hoc Fisher’s test *: P < 0.05). e, Variability of WM performance across all individual animals shown for the D-prime parameter computed during the OWM task. Examples of <t>exponential</t> fit (used to calculate the amplitude at 0 delay and the half-life) of a few individual performance curves are illustrated (red dashed lines). f-h, Comparison of WM performance amplitude at 0 delay and half-life for OWM and TSWM tasks (unpaired t-test). i, Cumulative probability distribution of half-lives for OWM and TSWM tasks (Kolmogorov- Smirnov test). Data in b , d , f-h are presented as mean ± SEM. See Supplementary Table 1 for detailed statistics.
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a, Schematic representation of the behavioral paradigm designed to evaluate olfactory and tactospatial WM (OWM and TSWM, respectively) in head-restrained mice. For each trial, both sample and target cues were presented for one second (see main text and methods for further details). b, Learning curves for OWM and TSWM tasks (fixed intercue delay of 5 and 1s, respectively; the number of mice is indicated; one-way repeated measures ANOVA). c, Change in performance between the first and the last 100 trials of the training phase shown in b for each mouse (black lines; paired t-test) and the mean population performance (colored bars). d, WM performances plotted as a function of the intercue delay duration (2WRM ANOVA, post-hoc Fisher’s test *: P < 0.05). e, Variability of WM performance across all individual animals shown for the D-prime parameter computed during the OWM task. Examples of exponential fit (used to calculate the amplitude at 0 delay and the half-life) of a few individual performance curves are illustrated (red dashed lines). f-h, Comparison of WM performance amplitude at 0 delay and half-life for OWM and TSWM tasks (unpaired t-test). i, Cumulative probability distribution of half-lives for OWM and TSWM tasks (Kolmogorov- Smirnov test). Data in b , d , f-h are presented as mean ± SEM. See Supplementary Table 1 for detailed statistics.

Journal: bioRxiv

Article Title: The claustrum is critical for maintaining working memory information

doi: 10.1101/2024.10.28.620649

Figure Lengend Snippet: a, Schematic representation of the behavioral paradigm designed to evaluate olfactory and tactospatial WM (OWM and TSWM, respectively) in head-restrained mice. For each trial, both sample and target cues were presented for one second (see main text and methods for further details). b, Learning curves for OWM and TSWM tasks (fixed intercue delay of 5 and 1s, respectively; the number of mice is indicated; one-way repeated measures ANOVA). c, Change in performance between the first and the last 100 trials of the training phase shown in b for each mouse (black lines; paired t-test) and the mean population performance (colored bars). d, WM performances plotted as a function of the intercue delay duration (2WRM ANOVA, post-hoc Fisher’s test *: P < 0.05). e, Variability of WM performance across all individual animals shown for the D-prime parameter computed during the OWM task. Examples of exponential fit (used to calculate the amplitude at 0 delay and the half-life) of a few individual performance curves are illustrated (red dashed lines). f-h, Comparison of WM performance amplitude at 0 delay and half-life for OWM and TSWM tasks (unpaired t-test). i, Cumulative probability distribution of half-lives for OWM and TSWM tasks (Kolmogorov- Smirnov test). Data in b , d , f-h are presented as mean ± SEM. See Supplementary Table 1 for detailed statistics.

Article Snippet: The accuracy and D-prime values of the WM tasks at varying delay durations were modeled using an exponential decay function in GraphPad Prism 9.0.

Techniques: Comparison