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datahigh software package  (MathWorks Inc)


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    MathWorks Inc datahigh software package
    Flow diagram for visualization of population activity. Dimensionality reduction is performed on high-dimensional population activity (n-d, where n is the number of neurons) to extract a latent space (k-d, where k is the number of latent variables). Typically, k is less than n but greater than 3. We can then use <t>DataHigh</t> to visualize many 2-d projections of the same latent space. Shown here are six different 2-d projections of the same 6-d (k = 6) latent space described in section 3.3.
    Datahigh Software Package, 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/datahigh software package/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    datahigh software package - by Bioz Stars, 2026-03
    90/100 stars

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    1) Product Images from "DataHigh: Graphical user interface for visualizing and interacting with high-dimensional neural activity"

    Article Title: DataHigh: Graphical user interface for visualizing and interacting with high-dimensional neural activity

    Journal: Journal of neural engineering

    doi: 10.1088/1741-2560/10/6/066012

    Flow diagram for visualization of population activity. Dimensionality reduction is performed on high-dimensional population activity (n-d, where n is the number of neurons) to extract a latent space (k-d, where k is the number of latent variables). Typically, k is less than n but greater than 3. We can then use DataHigh to visualize many 2-d projections of the same latent space. Shown here are six different 2-d projections of the same 6-d (k = 6) latent space described in section 3.3.
    Figure Legend Snippet: Flow diagram for visualization of population activity. Dimensionality reduction is performed on high-dimensional population activity (n-d, where n is the number of neurons) to extract a latent space (k-d, where k is the number of latent variables). Typically, k is less than n but greater than 3. We can then use DataHigh to visualize many 2-d projections of the same latent space. Shown here are six different 2-d projections of the same 6-d (k = 6) latent space described in section 3.3.

    Techniques Used: Activity Assay

    Flowchart for a data analysis procedure that utilizes visualization. The user may input raw spike trains into DataHigh, perform dimensionality reduction using the DimReduce tool (left-hand side of dimensionality reduction), and visualize many 2-d projections of the extracted latent space using DataHigh. The user may also perform dimensionality reduction outside the DataHigh environment (right-hand side of dimensionality reduction), and input the identified latent variables into DataHigh for visualization.
    Figure Legend Snippet: Flowchart for a data analysis procedure that utilizes visualization. The user may input raw spike trains into DataHigh, perform dimensionality reduction using the DimReduce tool (left-hand side of dimensionality reduction), and visualize many 2-d projections of the extracted latent space using DataHigh. The user may also perform dimensionality reduction outside the DataHigh environment (right-hand side of dimensionality reduction), and input the identified latent variables into DataHigh for visualization.

    Techniques Used:

    Main interface for DataHigh. Central panel: 2-d projection of 15-d single-trial neural trajectories extracted using GPFA from population activity recorded in premotor cortex during a standard delayed-reaching task for two different reach targets (green and blue) (section 3.2). Dots indicate time of target onset (red) and the go cue (cyan). Gray indicates baseline activity before stimulus onset. Preview panels (left and right of central panel): clicking and holding on a preview panel instantly rotates one of the two projection vectors that make up the central 2-d projection. The bottom right corner shows the percent variance of the latent space that is captured by the central 2-d projection. The Toolbar (far right) allows the user to access analysis tools described in section 2.3.
    Figure Legend Snippet: Main interface for DataHigh. Central panel: 2-d projection of 15-d single-trial neural trajectories extracted using GPFA from population activity recorded in premotor cortex during a standard delayed-reaching task for two different reach targets (green and blue) (section 3.2). Dots indicate time of target onset (red) and the go cue (cyan). Gray indicates baseline activity before stimulus onset. Preview panels (left and right of central panel): clicking and holding on a preview panel instantly rotates one of the two projection vectors that make up the central 2-d projection. The bottom right corner shows the percent variance of the latent space that is captured by the central 2-d projection. The Toolbar (far right) allows the user to access analysis tools described in section 2.3.

    Techniques Used: Activity Assay

    DimReduce allows the user to input raw spike trains, perform dimensionality reduction, choose the latent dimensionality, and upload the extracted latent variables to DataHigh. The large red “1” instructs the user where to complete the first step, which is to choose a bin width. Clicking the “Next Step” button increments the red step number and moves it to the next step. The example here shows a plot of leave-neuron-out prediction error versus candidate latent dimensionality. Using this metric, the optimal latent dimensionality is the dimensionality with the minimum cross-validated prediction error (starred on the plot).
    Figure Legend Snippet: DimReduce allows the user to input raw spike trains, perform dimensionality reduction, choose the latent dimensionality, and upload the extracted latent variables to DataHigh. The large red “1” instructs the user where to complete the first step, which is to choose a bin width. Clicking the “Next Step” button increments the red step number and moves it to the next step. The example here shows a plot of leave-neuron-out prediction error versus candidate latent dimensionality. Using this metric, the optimal latent dimensionality is the dimensionality with the minimum cross-validated prediction error (starred on the plot).

    Techniques Used:



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    datahigh software package  (MathWorks Inc)
    90
    MathWorks Inc datahigh software package
    Flow diagram for visualization of population activity. Dimensionality reduction is performed on high-dimensional population activity (n-d, where n is the number of neurons) to extract a latent space (k-d, where k is the number of latent variables). Typically, k is less than n but greater than 3. We can then use <t>DataHigh</t> to visualize many 2-d projections of the same latent space. Shown here are six different 2-d projections of the same 6-d (k = 6) latent space described in section 3.3.
    Datahigh Software Package, 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/datahigh software package/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    datahigh software package - by Bioz Stars, 2026-03
    90/100 stars
    		  Buy from Supplier

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    Flow diagram for visualization of population activity. Dimensionality reduction is performed on high-dimensional population activity (n-d, where n is the number of neurons) to extract a latent space (k-d, where k is the number of latent variables). Typically, k is less than n but greater than 3. We can then use DataHigh to visualize many 2-d projections of the same latent space. Shown here are six different 2-d projections of the same 6-d (k = 6) latent space described in section 3.3.

    Journal: Journal of neural engineering

    Article Title: DataHigh: Graphical user interface for visualizing and interacting with high-dimensional neural activity

    doi: 10.1088/1741-2560/10/6/066012

    Figure Lengend Snippet: Flow diagram for visualization of population activity. Dimensionality reduction is performed on high-dimensional population activity (n-d, where n is the number of neurons) to extract a latent space (k-d, where k is the number of latent variables). Typically, k is less than n but greater than 3. We can then use DataHigh to visualize many 2-d projections of the same latent space. Shown here are six different 2-d projections of the same 6-d (k = 6) latent space described in section 3.3.

    Article Snippet: The DataHigh software package for Matlab can be downloaded from http://www.ece.cmu.edu/~byronyu/software.shtml .

    Techniques: Activity Assay

    Flowchart for a data analysis procedure that utilizes visualization. The user may input raw spike trains into DataHigh, perform dimensionality reduction using the DimReduce tool (left-hand side of dimensionality reduction), and visualize many 2-d projections of the extracted latent space using DataHigh. The user may also perform dimensionality reduction outside the DataHigh environment (right-hand side of dimensionality reduction), and input the identified latent variables into DataHigh for visualization.

    Journal: Journal of neural engineering

    Article Title: DataHigh: Graphical user interface for visualizing and interacting with high-dimensional neural activity

    doi: 10.1088/1741-2560/10/6/066012

    Figure Lengend Snippet: Flowchart for a data analysis procedure that utilizes visualization. The user may input raw spike trains into DataHigh, perform dimensionality reduction using the DimReduce tool (left-hand side of dimensionality reduction), and visualize many 2-d projections of the extracted latent space using DataHigh. The user may also perform dimensionality reduction outside the DataHigh environment (right-hand side of dimensionality reduction), and input the identified latent variables into DataHigh for visualization.

    Article Snippet: The DataHigh software package for Matlab can be downloaded from http://www.ece.cmu.edu/~byronyu/software.shtml .

    Techniques:

    Main interface for DataHigh. Central panel: 2-d projection of 15-d single-trial neural trajectories extracted using GPFA from population activity recorded in premotor cortex during a standard delayed-reaching task for two different reach targets (green and blue) (section 3.2). Dots indicate time of target onset (red) and the go cue (cyan). Gray indicates baseline activity before stimulus onset. Preview panels (left and right of central panel): clicking and holding on a preview panel instantly rotates one of the two projection vectors that make up the central 2-d projection. The bottom right corner shows the percent variance of the latent space that is captured by the central 2-d projection. The Toolbar (far right) allows the user to access analysis tools described in section 2.3.

    Journal: Journal of neural engineering

    Article Title: DataHigh: Graphical user interface for visualizing and interacting with high-dimensional neural activity

    doi: 10.1088/1741-2560/10/6/066012

    Figure Lengend Snippet: Main interface for DataHigh. Central panel: 2-d projection of 15-d single-trial neural trajectories extracted using GPFA from population activity recorded in premotor cortex during a standard delayed-reaching task for two different reach targets (green and blue) (section 3.2). Dots indicate time of target onset (red) and the go cue (cyan). Gray indicates baseline activity before stimulus onset. Preview panels (left and right of central panel): clicking and holding on a preview panel instantly rotates one of the two projection vectors that make up the central 2-d projection. The bottom right corner shows the percent variance of the latent space that is captured by the central 2-d projection. The Toolbar (far right) allows the user to access analysis tools described in section 2.3.

    Article Snippet: The DataHigh software package for Matlab can be downloaded from http://www.ece.cmu.edu/~byronyu/software.shtml .

    Techniques: Activity Assay

    DimReduce allows the user to input raw spike trains, perform dimensionality reduction, choose the latent dimensionality, and upload the extracted latent variables to DataHigh. The large red “1” instructs the user where to complete the first step, which is to choose a bin width. Clicking the “Next Step” button increments the red step number and moves it to the next step. The example here shows a plot of leave-neuron-out prediction error versus candidate latent dimensionality. Using this metric, the optimal latent dimensionality is the dimensionality with the minimum cross-validated prediction error (starred on the plot).

    Journal: Journal of neural engineering

    Article Title: DataHigh: Graphical user interface for visualizing and interacting with high-dimensional neural activity

    doi: 10.1088/1741-2560/10/6/066012

    Figure Lengend Snippet: DimReduce allows the user to input raw spike trains, perform dimensionality reduction, choose the latent dimensionality, and upload the extracted latent variables to DataHigh. The large red “1” instructs the user where to complete the first step, which is to choose a bin width. Clicking the “Next Step” button increments the red step number and moves it to the next step. The example here shows a plot of leave-neuron-out prediction error versus candidate latent dimensionality. Using this metric, the optimal latent dimensionality is the dimensionality with the minimum cross-validated prediction error (starred on the plot).

    Article Snippet: The DataHigh software package for Matlab can be downloaded from http://www.ece.cmu.edu/~byronyu/software.shtml .

    Techniques: