Review



lda using matlab code  (MathWorks Inc)


Bioz Verified Symbol MathWorks Inc is a verified supplier  
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 90
      Buy from Supplier

    Structured Review

    MathWorks Inc lda using matlab code
    The accuracy for decoding the contextual identity of the odorant <t>from</t> <t>tPRP</t> decreased in the CaMKIIα knock-out mouse and was correlated with percent correct discrimination. A , B , Examples for one mouse of the time course for the accuracy of odorant identification by <t>LDA</t> trained using CA1 tPRP for the EAPA odor pair. A , β tPRP. B , γ tPRP. (i) WT. (ii) Het. (iii) KO. Shadow: confidence interval, black bar: odorant application. C , D , Bar graphs showing the differences in discriminant accuracy between the different genotypes. C , Accuracy for peak tPRP for (i) θ/β in the hippocampus, (ii) θ/γ in the hippocampus, (iii) θ/β in mPFC, (iv) θ/γ in mPFC. D , Accuracy for through for (i) θ/β in the hippocampus, (ii) θ/γ in the hippocampus, (iii) θ/β in mPFC, (iv) θ/γ mPFC. The bars show the average accuracy, and the points are the accuracy per mouse per odor pair. The vertical bars show the confidence interval. For β tPRP LDA GLM found statistically significant differences between WT and KO for all conditions ( p < 0.001) and between WT and Het for γ trough tPRP ( p < 0.05, 756 observations, 744 df, F statistic = 9.9–34.3, p < 0.001, 6 mice, 8 odor pairs; Extended Data ). Asterisks show significant p values ( p <pFDR) for post hoc pairwise tests. E , Relationship for proficient mice between percent correct in the go-no go behavior and accuracy of odor identification by the LDA decoding algorithm shown per mouse per odor pair (6 mice, 8 odor pairs). The correlation coefficients were: E (i) 0.3, E (ii) 0.24, E (iii) 0.29, E (iv) 0.23, F (i) 0.3, F (ii) 0.36, F (iii) 0.29, F (iv) 0.40, and the p value for significance was p < 0.01. Lines are best fit lines. Extended Data provides GLM statistical analysis for the data in C–F .
    Lda Using Matlab Code, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/lda using matlab code/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    lda using matlab code - by Bioz Stars, 2026-03
    90/100 stars

    Images

    1) Product Images from "Hippocampal-Prefrontal θ Coupling Develops as Mice Become Proficient in Associative Odorant Discrimination Learning"

    Article Title: Hippocampal-Prefrontal θ Coupling Develops as Mice Become Proficient in Associative Odorant Discrimination Learning

    Journal: eNeuro

    doi: 10.1523/ENEURO.0259-22.2022

    The accuracy for decoding the contextual identity of the odorant from tPRP decreased in the CaMKIIα knock-out mouse and was correlated with percent correct discrimination. A , B , Examples for one mouse of the time course for the accuracy of odorant identification by LDA trained using CA1 tPRP for the EAPA odor pair. A , β tPRP. B , γ tPRP. (i) WT. (ii) Het. (iii) KO. Shadow: confidence interval, black bar: odorant application. C , D , Bar graphs showing the differences in discriminant accuracy between the different genotypes. C , Accuracy for peak tPRP for (i) θ/β in the hippocampus, (ii) θ/γ in the hippocampus, (iii) θ/β in mPFC, (iv) θ/γ in mPFC. D , Accuracy for through for (i) θ/β in the hippocampus, (ii) θ/γ in the hippocampus, (iii) θ/β in mPFC, (iv) θ/γ mPFC. The bars show the average accuracy, and the points are the accuracy per mouse per odor pair. The vertical bars show the confidence interval. For β tPRP LDA GLM found statistically significant differences between WT and KO for all conditions ( p < 0.001) and between WT and Het for γ trough tPRP ( p < 0.05, 756 observations, 744 df, F statistic = 9.9–34.3, p < 0.001, 6 mice, 8 odor pairs; Extended Data ). Asterisks show significant p values ( p <pFDR) for post hoc pairwise tests. E , Relationship for proficient mice between percent correct in the go-no go behavior and accuracy of odor identification by the LDA decoding algorithm shown per mouse per odor pair (6 mice, 8 odor pairs). The correlation coefficients were: E (i) 0.3, E (ii) 0.24, E (iii) 0.29, E (iv) 0.23, F (i) 0.3, F (ii) 0.36, F (iii) 0.29, F (iv) 0.40, and the p value for significance was p < 0.01. Lines are best fit lines. Extended Data provides GLM statistical analysis for the data in C–F .
    Figure Legend Snippet: The accuracy for decoding the contextual identity of the odorant from tPRP decreased in the CaMKIIα knock-out mouse and was correlated with percent correct discrimination. A , B , Examples for one mouse of the time course for the accuracy of odorant identification by LDA trained using CA1 tPRP for the EAPA odor pair. A , β tPRP. B , γ tPRP. (i) WT. (ii) Het. (iii) KO. Shadow: confidence interval, black bar: odorant application. C , D , Bar graphs showing the differences in discriminant accuracy between the different genotypes. C , Accuracy for peak tPRP for (i) θ/β in the hippocampus, (ii) θ/γ in the hippocampus, (iii) θ/β in mPFC, (iv) θ/γ in mPFC. D , Accuracy for through for (i) θ/β in the hippocampus, (ii) θ/γ in the hippocampus, (iii) θ/β in mPFC, (iv) θ/γ mPFC. The bars show the average accuracy, and the points are the accuracy per mouse per odor pair. The vertical bars show the confidence interval. For β tPRP LDA GLM found statistically significant differences between WT and KO for all conditions ( p < 0.001) and between WT and Het for γ trough tPRP ( p < 0.05, 756 observations, 744 df, F statistic = 9.9–34.3, p < 0.001, 6 mice, 8 odor pairs; Extended Data ). Asterisks show significant p values ( p

    Techniques Used: Knock-Out

    LDA for decoding the contextual odorant identity from tPRP. A , Example for one mouse for the time course for the accuracy of odorant identity decoding by a LDA algorithm trained using tPRP calculated from CA1 LFP for the EAPA odor pair (i) naive θ/β, θ (ii) proficient θ/β, (iii) naive θ/γ, (iv) proficient θ/γ red: peak, blue: through, black: shuffled, shadow: confidence interval, black bar: odorant application. B , C , Bar graphs showing the differences in decoding accuracy between shuffled, naive, and proficient. B , Accuracy for peak tPRP for (i) θ/β in the hippocampus, (ii) θ/γ in the hippocampus, (iii) θ/β in mPFC, (iv) θ/γ in mPFC. C , Accuracy for through for (i) θ/β in the hippocampus, (ii) θ/γ in the hippocampus, (iii) θ/β in mPFC, (iv) θ/γ mPFC. The bars show the average accuracy, and the points are the accuracy per mouse per odor pair. The vertical bars show the confidence interval. The gray symbols and lines are per mouse averages. For β and γ tPRP for both prefrontal and hippocampus LDA, GLM found statistically significant differences for naive versus proficient and shuffled versus proficient ( p < 0.001, 380 observations, 372 df, F statistic = 355–494, p < 0.001, 6 mice, 8 odor pairs; Extended Data ). For γ tPRP for both prefrontal and hippocampus LDA, GLM found statistically significant differences between peak and trough ( p < 0.05, 380 observations, 372 df, F statistic = 355–494, p < 0.001, 6 mice, 8 odor pairs; Extended Data ). Asterisks show significant p values ( p <pFDR) for post hoc pairwise tests. Extended Data provides GLM and ANOVAN statistical analysis for the data in B and C .
    Figure Legend Snippet: LDA for decoding the contextual odorant identity from tPRP. A , Example for one mouse for the time course for the accuracy of odorant identity decoding by a LDA algorithm trained using tPRP calculated from CA1 LFP for the EAPA odor pair (i) naive θ/β, θ (ii) proficient θ/β, (iii) naive θ/γ, (iv) proficient θ/γ red: peak, blue: through, black: shuffled, shadow: confidence interval, black bar: odorant application. B , C , Bar graphs showing the differences in decoding accuracy between shuffled, naive, and proficient. B , Accuracy for peak tPRP for (i) θ/β in the hippocampus, (ii) θ/γ in the hippocampus, (iii) θ/β in mPFC, (iv) θ/γ in mPFC. C , Accuracy for through for (i) θ/β in the hippocampus, (ii) θ/γ in the hippocampus, (iii) θ/β in mPFC, (iv) θ/γ mPFC. The bars show the average accuracy, and the points are the accuracy per mouse per odor pair. The vertical bars show the confidence interval. The gray symbols and lines are per mouse averages. For β and γ tPRP for both prefrontal and hippocampus LDA, GLM found statistically significant differences for naive versus proficient and shuffled versus proficient ( p < 0.001, 380 observations, 372 df, F statistic = 355–494, p < 0.001, 6 mice, 8 odor pairs; Extended Data ). For γ tPRP for both prefrontal and hippocampus LDA, GLM found statistically significant differences between peak and trough ( p < 0.05, 380 observations, 372 df, F statistic = 355–494, p < 0.001, 6 mice, 8 odor pairs; Extended Data ). Asterisks show significant p values ( p

    Techniques Used:



    Similar Products

    lda using matlab code  (MathWorks Inc)
    90
    MathWorks Inc lda using matlab code
    The accuracy for decoding the contextual identity of the odorant <t>from</t> <t>tPRP</t> decreased in the CaMKIIα knock-out mouse and was correlated with percent correct discrimination. A , B , Examples for one mouse of the time course for the accuracy of odorant identification by <t>LDA</t> trained using CA1 tPRP for the EAPA odor pair. A , β tPRP. B , γ tPRP. (i) WT. (ii) Het. (iii) KO. Shadow: confidence interval, black bar: odorant application. C , D , Bar graphs showing the differences in discriminant accuracy between the different genotypes. C , Accuracy for peak tPRP for (i) θ/β in the hippocampus, (ii) θ/γ in the hippocampus, (iii) θ/β in mPFC, (iv) θ/γ in mPFC. D , Accuracy for through for (i) θ/β in the hippocampus, (ii) θ/γ in the hippocampus, (iii) θ/β in mPFC, (iv) θ/γ mPFC. The bars show the average accuracy, and the points are the accuracy per mouse per odor pair. The vertical bars show the confidence interval. For β tPRP LDA GLM found statistically significant differences between WT and KO for all conditions ( p < 0.001) and between WT and Het for γ trough tPRP ( p < 0.05, 756 observations, 744 df, F statistic = 9.9–34.3, p < 0.001, 6 mice, 8 odor pairs; Extended Data ). Asterisks show significant p values ( p <pFDR) for post hoc pairwise tests. E , Relationship for proficient mice between percent correct in the go-no go behavior and accuracy of odor identification by the LDA decoding algorithm shown per mouse per odor pair (6 mice, 8 odor pairs). The correlation coefficients were: E (i) 0.3, E (ii) 0.24, E (iii) 0.29, E (iv) 0.23, F (i) 0.3, F (ii) 0.36, F (iii) 0.29, F (iv) 0.40, and the p value for significance was p < 0.01. Lines are best fit lines. Extended Data provides GLM statistical analysis for the data in C–F .
    Lda Using Matlab Code, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/lda using matlab code/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    lda using matlab code - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier
    lda for classification using matlab code 2019a  (MathWorks Inc)
    90
    MathWorks Inc lda for classification using matlab code 2019a
    The accuracy for decoding the contextual identity of the odorant <t>from</t> <t>tPRP</t> decreased in the CaMKIIα knock-out mouse and was correlated with percent correct discrimination. A , B , Examples for one mouse of the time course for the accuracy of odorant identification by <t>LDA</t> trained using CA1 tPRP for the EAPA odor pair. A , β tPRP. B , γ tPRP. (i) WT. (ii) Het. (iii) KO. Shadow: confidence interval, black bar: odorant application. C , D , Bar graphs showing the differences in discriminant accuracy between the different genotypes. C , Accuracy for peak tPRP for (i) θ/β in the hippocampus, (ii) θ/γ in the hippocampus, (iii) θ/β in mPFC, (iv) θ/γ in mPFC. D , Accuracy for through for (i) θ/β in the hippocampus, (ii) θ/γ in the hippocampus, (iii) θ/β in mPFC, (iv) θ/γ mPFC. The bars show the average accuracy, and the points are the accuracy per mouse per odor pair. The vertical bars show the confidence interval. For β tPRP LDA GLM found statistically significant differences between WT and KO for all conditions ( p < 0.001) and between WT and Het for γ trough tPRP ( p < 0.05, 756 observations, 744 df, F statistic = 9.9–34.3, p < 0.001, 6 mice, 8 odor pairs; Extended Data ). Asterisks show significant p values ( p <pFDR) for post hoc pairwise tests. E , Relationship for proficient mice between percent correct in the go-no go behavior and accuracy of odor identification by the LDA decoding algorithm shown per mouse per odor pair (6 mice, 8 odor pairs). The correlation coefficients were: E (i) 0.3, E (ii) 0.24, E (iii) 0.29, E (iv) 0.23, F (i) 0.3, F (ii) 0.36, F (iii) 0.29, F (iv) 0.40, and the p value for significance was p < 0.01. Lines are best fit lines. Extended Data provides GLM statistical analysis for the data in C–F .
    Lda For Classification Using Matlab Code 2019a, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/lda for classification using matlab code 2019a/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    lda for classification using matlab code 2019a - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    The accuracy for decoding the contextual identity of the odorant from tPRP decreased in the CaMKIIα knock-out mouse and was correlated with percent correct discrimination. A , B , Examples for one mouse of the time course for the accuracy of odorant identification by LDA trained using CA1 tPRP for the EAPA odor pair. A , β tPRP. B , γ tPRP. (i) WT. (ii) Het. (iii) KO. Shadow: confidence interval, black bar: odorant application. C , D , Bar graphs showing the differences in discriminant accuracy between the different genotypes. C , Accuracy for peak tPRP for (i) θ/β in the hippocampus, (ii) θ/γ in the hippocampus, (iii) θ/β in mPFC, (iv) θ/γ in mPFC. D , Accuracy for through for (i) θ/β in the hippocampus, (ii) θ/γ in the hippocampus, (iii) θ/β in mPFC, (iv) θ/γ mPFC. The bars show the average accuracy, and the points are the accuracy per mouse per odor pair. The vertical bars show the confidence interval. For β tPRP LDA GLM found statistically significant differences between WT and KO for all conditions ( p < 0.001) and between WT and Het for γ trough tPRP ( p < 0.05, 756 observations, 744 df, F statistic = 9.9–34.3, p < 0.001, 6 mice, 8 odor pairs; Extended Data ). Asterisks show significant p values ( p <pFDR) for post hoc pairwise tests. E , Relationship for proficient mice between percent correct in the go-no go behavior and accuracy of odor identification by the LDA decoding algorithm shown per mouse per odor pair (6 mice, 8 odor pairs). The correlation coefficients were: E (i) 0.3, E (ii) 0.24, E (iii) 0.29, E (iv) 0.23, F (i) 0.3, F (ii) 0.36, F (iii) 0.29, F (iv) 0.40, and the p value for significance was p < 0.01. Lines are best fit lines. Extended Data provides GLM statistical analysis for the data in C–F .

    Journal: eNeuro

    Article Title: Hippocampal-Prefrontal θ Coupling Develops as Mice Become Proficient in Associative Odorant Discrimination Learning

    doi: 10.1523/ENEURO.0259-22.2022

    Figure Lengend Snippet: The accuracy for decoding the contextual identity of the odorant from tPRP decreased in the CaMKIIα knock-out mouse and was correlated with percent correct discrimination. A , B , Examples for one mouse of the time course for the accuracy of odorant identification by LDA trained using CA1 tPRP for the EAPA odor pair. A , β tPRP. B , γ tPRP. (i) WT. (ii) Het. (iii) KO. Shadow: confidence interval, black bar: odorant application. C , D , Bar graphs showing the differences in discriminant accuracy between the different genotypes. C , Accuracy for peak tPRP for (i) θ/β in the hippocampus, (ii) θ/γ in the hippocampus, (iii) θ/β in mPFC, (iv) θ/γ in mPFC. D , Accuracy for through for (i) θ/β in the hippocampus, (ii) θ/γ in the hippocampus, (iii) θ/β in mPFC, (iv) θ/γ mPFC. The bars show the average accuracy, and the points are the accuracy per mouse per odor pair. The vertical bars show the confidence interval. For β tPRP LDA GLM found statistically significant differences between WT and KO for all conditions ( p < 0.001) and between WT and Het for γ trough tPRP ( p < 0.05, 756 observations, 744 df, F statistic = 9.9–34.3, p < 0.001, 6 mice, 8 odor pairs; Extended Data ). Asterisks show significant p values ( p

    Article Snippet: Decoding of contextual odorant identity from tPRP values was performed using LDA using MATLAB code as described by .

    Techniques: Knock-Out

    LDA for decoding the contextual odorant identity from tPRP. A , Example for one mouse for the time course for the accuracy of odorant identity decoding by a LDA algorithm trained using tPRP calculated from CA1 LFP for the EAPA odor pair (i) naive θ/β, θ (ii) proficient θ/β, (iii) naive θ/γ, (iv) proficient θ/γ red: peak, blue: through, black: shuffled, shadow: confidence interval, black bar: odorant application. B , C , Bar graphs showing the differences in decoding accuracy between shuffled, naive, and proficient. B , Accuracy for peak tPRP for (i) θ/β in the hippocampus, (ii) θ/γ in the hippocampus, (iii) θ/β in mPFC, (iv) θ/γ in mPFC. C , Accuracy for through for (i) θ/β in the hippocampus, (ii) θ/γ in the hippocampus, (iii) θ/β in mPFC, (iv) θ/γ mPFC. The bars show the average accuracy, and the points are the accuracy per mouse per odor pair. The vertical bars show the confidence interval. The gray symbols and lines are per mouse averages. For β and γ tPRP for both prefrontal and hippocampus LDA, GLM found statistically significant differences for naive versus proficient and shuffled versus proficient ( p < 0.001, 380 observations, 372 df, F statistic = 355–494, p < 0.001, 6 mice, 8 odor pairs; Extended Data ). For γ tPRP for both prefrontal and hippocampus LDA, GLM found statistically significant differences between peak and trough ( p < 0.05, 380 observations, 372 df, F statistic = 355–494, p < 0.001, 6 mice, 8 odor pairs; Extended Data ). Asterisks show significant p values ( p <pFDR) for post hoc pairwise tests. Extended Data provides GLM and ANOVAN statistical analysis for the data in B and C .

    Journal: eNeuro

    Article Title: Hippocampal-Prefrontal θ Coupling Develops as Mice Become Proficient in Associative Odorant Discrimination Learning

    doi: 10.1523/ENEURO.0259-22.2022

    Figure Lengend Snippet: LDA for decoding the contextual odorant identity from tPRP. A , Example for one mouse for the time course for the accuracy of odorant identity decoding by a LDA algorithm trained using tPRP calculated from CA1 LFP for the EAPA odor pair (i) naive θ/β, θ (ii) proficient θ/β, (iii) naive θ/γ, (iv) proficient θ/γ red: peak, blue: through, black: shuffled, shadow: confidence interval, black bar: odorant application. B , C , Bar graphs showing the differences in decoding accuracy between shuffled, naive, and proficient. B , Accuracy for peak tPRP for (i) θ/β in the hippocampus, (ii) θ/γ in the hippocampus, (iii) θ/β in mPFC, (iv) θ/γ in mPFC. C , Accuracy for through for (i) θ/β in the hippocampus, (ii) θ/γ in the hippocampus, (iii) θ/β in mPFC, (iv) θ/γ mPFC. The bars show the average accuracy, and the points are the accuracy per mouse per odor pair. The vertical bars show the confidence interval. The gray symbols and lines are per mouse averages. For β and γ tPRP for both prefrontal and hippocampus LDA, GLM found statistically significant differences for naive versus proficient and shuffled versus proficient ( p < 0.001, 380 observations, 372 df, F statistic = 355–494, p < 0.001, 6 mice, 8 odor pairs; Extended Data ). For γ tPRP for both prefrontal and hippocampus LDA, GLM found statistically significant differences between peak and trough ( p < 0.05, 380 observations, 372 df, F statistic = 355–494, p < 0.001, 6 mice, 8 odor pairs; Extended Data ). Asterisks show significant p values ( p

    Article Snippet: Decoding of contextual odorant identity from tPRP values was performed using LDA using MATLAB code as described by .

    Techniques: