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pearson correlations matlab function corr  (MathWorks Inc)


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    MathWorks Inc pearson correlations matlab function corr
    a Venn diagrams depicting the percent of the total pseudopopulation in each region that was selective for the sample location around the poke initiating each task phase. Overlapping circles in the Venn diagram represent the percent of neurons that share retrospective sample location selectivity around either two or all three task phase pokes. b Example GLM-derived beta-weight histograms from significant sample location-encoding neurons in the GLM showing how most neurons in the MOs remain selective for the same port across sample and delay pokes (top), but neurons in the vmPFC switch initial sample location selectivity from sample to delay (bottom). c <t>Pearson</t> beta-weight correlations quantifying the similarity in location selectivity between pokes initiating one task phase, to pokes initiating another one. MOs location selectivity is similar from sample to delay pokes, but destabilizes over the course of the task. dmPFC remains the most stable across time. vmPFC shifts location selectivity from the sample to delay pokes, but then stabilizes later in the task. Asterisks represent an r value that is significantly different from zero, while Orth ( Orth ogonal) represents an r value that is not significantly different from zero. The reason for the non-significant r value from the vmPFC correlation in the Delay Poke vs Late Delay condition is because of the much smaller number of selective neurons in vmPFC during this time. Numbers above each bar indicate the count of significantly selective neurons in each region used for the correlation.
    Pearson Correlations Matlab Function Corr, 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
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    1) Product Images from "Divergent subregional information processing in mouse prefrontal cortex during working memory"

    Article Title: Divergent subregional information processing in mouse prefrontal cortex during working memory

    Journal: Communications Biology

    doi: 10.1038/s42003-024-06926-8

    a Venn diagrams depicting the percent of the total pseudopopulation in each region that was selective for the sample location around the poke initiating each task phase. Overlapping circles in the Venn diagram represent the percent of neurons that share retrospective sample location selectivity around either two or all three task phase pokes. b Example GLM-derived beta-weight histograms from significant sample location-encoding neurons in the GLM showing how most neurons in the MOs remain selective for the same port across sample and delay pokes (top), but neurons in the vmPFC switch initial sample location selectivity from sample to delay (bottom). c Pearson beta-weight correlations quantifying the similarity in location selectivity between pokes initiating one task phase, to pokes initiating another one. MOs location selectivity is similar from sample to delay pokes, but destabilizes over the course of the task. dmPFC remains the most stable across time. vmPFC shifts location selectivity from the sample to delay pokes, but then stabilizes later in the task. Asterisks represent an r value that is significantly different from zero, while Orth ( Orth ogonal) represents an r value that is not significantly different from zero. The reason for the non-significant r value from the vmPFC correlation in the Delay Poke vs Late Delay condition is because of the much smaller number of selective neurons in vmPFC during this time. Numbers above each bar indicate the count of significantly selective neurons in each region used for the correlation.
    Figure Legend Snippet: a Venn diagrams depicting the percent of the total pseudopopulation in each region that was selective for the sample location around the poke initiating each task phase. Overlapping circles in the Venn diagram represent the percent of neurons that share retrospective sample location selectivity around either two or all three task phase pokes. b Example GLM-derived beta-weight histograms from significant sample location-encoding neurons in the GLM showing how most neurons in the MOs remain selective for the same port across sample and delay pokes (top), but neurons in the vmPFC switch initial sample location selectivity from sample to delay (bottom). c Pearson beta-weight correlations quantifying the similarity in location selectivity between pokes initiating one task phase, to pokes initiating another one. MOs location selectivity is similar from sample to delay pokes, but destabilizes over the course of the task. dmPFC remains the most stable across time. vmPFC shifts location selectivity from the sample to delay pokes, but then stabilizes later in the task. Asterisks represent an r value that is significantly different from zero, while Orth ( Orth ogonal) represents an r value that is not significantly different from zero. The reason for the non-significant r value from the vmPFC correlation in the Delay Poke vs Late Delay condition is because of the much smaller number of selective neurons in vmPFC during this time. Numbers above each bar indicate the count of significantly selective neurons in each region used for the correlation.

    Techniques Used: Derivative Assay

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    Article Snippet: These analysis were performed using Pearson correlations as implemented in the ‘corr’ function of MATLAB 2021a (Mathworks, Natick, MA, United States).

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    Article Title: Healthful choices depend on the latency and rate of information accumulation.
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    Plasmid Preparation:

    Article Title: Divergent subregional information processing in mouse prefrontal cortex during working memory
    Article Snippet: .. We quantified how these beta-weights changed over time using Pearson correlations ( r , MATLAB function corr ) comparing the significant beta-weight population vector at one time point to the beta-weight population of those same neurons at a future time point. ..



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    a Venn diagrams depicting the percent of the total pseudopopulation in each region that was selective for the sample location around the poke initiating each task phase. Overlapping circles in the Venn diagram represent the percent of neurons that share retrospective sample location selectivity around either two or all three task phase pokes. b Example GLM-derived beta-weight histograms from significant sample location-encoding neurons in the GLM showing how most neurons in the MOs remain selective for the same port across sample and delay pokes (top), but neurons in the vmPFC switch initial sample location selectivity from sample to delay (bottom). c <t>Pearson</t> beta-weight correlations quantifying the similarity in location selectivity between pokes initiating one task phase, to pokes initiating another one. MOs location selectivity is similar from sample to delay pokes, but destabilizes over the course of the task. dmPFC remains the most stable across time. vmPFC shifts location selectivity from the sample to delay pokes, but then stabilizes later in the task. Asterisks represent an r value that is significantly different from zero, while Orth ( Orth ogonal) represents an r value that is not significantly different from zero. The reason for the non-significant r value from the vmPFC correlation in the Delay Poke vs Late Delay condition is because of the much smaller number of selective neurons in vmPFC during this time. Numbers above each bar indicate the count of significantly selective neurons in each region used for the correlation.
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    a Venn diagrams depicting the percent of the total pseudopopulation in each region that was selective for the sample location around the poke initiating each task phase. Overlapping circles in the Venn diagram represent the percent of neurons that share retrospective sample location selectivity around either two or all three task phase pokes. b Example GLM-derived beta-weight histograms from significant sample location-encoding neurons in the GLM showing how most neurons in the MOs remain selective for the same port across sample and delay pokes (top), but neurons in the vmPFC switch initial sample location selectivity from sample to delay (bottom). c <t>Pearson</t> beta-weight correlations quantifying the similarity in location selectivity between pokes initiating one task phase, to pokes initiating another one. MOs location selectivity is similar from sample to delay pokes, but destabilizes over the course of the task. dmPFC remains the most stable across time. vmPFC shifts location selectivity from the sample to delay pokes, but then stabilizes later in the task. Asterisks represent an r value that is significantly different from zero, while Orth ( Orth ogonal) represents an r value that is not significantly different from zero. The reason for the non-significant r value from the vmPFC correlation in the Delay Poke vs Late Delay condition is because of the much smaller number of selective neurons in vmPFC during this time. Numbers above each bar indicate the count of significantly selective neurons in each region used for the correlation.
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    a Venn diagrams depicting the percent of the total pseudopopulation in each region that was selective for the sample location around the poke initiating each task phase. Overlapping circles in the Venn diagram represent the percent of neurons that share retrospective sample location selectivity around either two or all three task phase pokes. b Example GLM-derived beta-weight histograms from significant sample location-encoding neurons in the GLM showing how most neurons in the MOs remain selective for the same port across sample and delay pokes (top), but neurons in the vmPFC switch initial sample location selectivity from sample to delay (bottom). c <t>Pearson</t> beta-weight correlations quantifying the similarity in location selectivity between pokes initiating one task phase, to pokes initiating another one. MOs location selectivity is similar from sample to delay pokes, but destabilizes over the course of the task. dmPFC remains the most stable across time. vmPFC shifts location selectivity from the sample to delay pokes, but then stabilizes later in the task. Asterisks represent an r value that is significantly different from zero, while Orth ( Orth ogonal) represents an r value that is not significantly different from zero. The reason for the non-significant r value from the vmPFC correlation in the Delay Poke vs Late Delay condition is because of the much smaller number of selective neurons in vmPFC during this time. Numbers above each bar indicate the count of significantly selective neurons in each region used for the correlation.
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    a Venn diagrams depicting the percent of the total pseudopopulation in each region that was selective for the sample location around the poke initiating each task phase. Overlapping circles in the Venn diagram represent the percent of neurons that share retrospective sample location selectivity around either two or all three task phase pokes. b Example GLM-derived beta-weight histograms from significant sample location-encoding neurons in the GLM showing how most neurons in the MOs remain selective for the same port across sample and delay pokes (top), but neurons in the vmPFC switch initial sample location selectivity from sample to delay (bottom). c Pearson beta-weight correlations quantifying the similarity in location selectivity between pokes initiating one task phase, to pokes initiating another one. MOs location selectivity is similar from sample to delay pokes, but destabilizes over the course of the task. dmPFC remains the most stable across time. vmPFC shifts location selectivity from the sample to delay pokes, but then stabilizes later in the task. Asterisks represent an r value that is significantly different from zero, while Orth ( Orth ogonal) represents an r value that is not significantly different from zero. The reason for the non-significant r value from the vmPFC correlation in the Delay Poke vs Late Delay condition is because of the much smaller number of selective neurons in vmPFC during this time. Numbers above each bar indicate the count of significantly selective neurons in each region used for the correlation.

    Journal: Communications Biology

    Article Title: Divergent subregional information processing in mouse prefrontal cortex during working memory

    doi: 10.1038/s42003-024-06926-8

    Figure Lengend Snippet: a Venn diagrams depicting the percent of the total pseudopopulation in each region that was selective for the sample location around the poke initiating each task phase. Overlapping circles in the Venn diagram represent the percent of neurons that share retrospective sample location selectivity around either two or all three task phase pokes. b Example GLM-derived beta-weight histograms from significant sample location-encoding neurons in the GLM showing how most neurons in the MOs remain selective for the same port across sample and delay pokes (top), but neurons in the vmPFC switch initial sample location selectivity from sample to delay (bottom). c Pearson beta-weight correlations quantifying the similarity in location selectivity between pokes initiating one task phase, to pokes initiating another one. MOs location selectivity is similar from sample to delay pokes, but destabilizes over the course of the task. dmPFC remains the most stable across time. vmPFC shifts location selectivity from the sample to delay pokes, but then stabilizes later in the task. Asterisks represent an r value that is significantly different from zero, while Orth ( Orth ogonal) represents an r value that is not significantly different from zero. The reason for the non-significant r value from the vmPFC correlation in the Delay Poke vs Late Delay condition is because of the much smaller number of selective neurons in vmPFC during this time. Numbers above each bar indicate the count of significantly selective neurons in each region used for the correlation.

    Article Snippet: We quantified how these beta-weights changed over time using Pearson correlations ( r , MATLAB function corr ) comparing the significant beta-weight population vector at one time point to the beta-weight population of those same neurons at a future time point.

    Techniques: Derivative Assay