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glmfit generalized linear model (glm) fit function/subroutine  (MathWorks Inc)


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    MathWorks Inc glmfit generalized linear model (glm) fit function/subroutine
    Glmfit Generalized Linear Model (Glm) Fit Function/Subroutine, 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/product/general+linear+model+fitting+function/pm24326336-129-9-17
    Average 90 stars, based on 1 article reviews
    glmfit generalized linear model (glm) fit function/subroutine - by Bioz Stars, 2026-09
    90/100 stars

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    other:

    Article Title: Dynamic tracking of objects in the macaque dorsomedial frontal cortex
    Article Snippet: We separately fitted the GLM for two task epochs using the ‘glmfit’ function in MATLAB, assuming a normal distribution for the noise in the mean spike counts.

    Article Title: Employment of time-varying sensory evidence to test the mechanisms underlying flexible decision-making
    Article Snippet: We used the ‘glmfit’ function in MATLAB to compute the regression coefficients, their standard errors, and the associated P values.

    Article Title: Orexin neurons track temporal features of blood glucose in behaving mice.
    Article Snippet: Resultant data were fit using the ‘glmfit’ function in MATLAB by bootstrapping 1/4 experiment-duration chunks randomly over 2,000 iterations.

    Article Title: Social status mediates the propagation of unfairness
    Article Snippet: We also examined the individual differences in the effect of DG1 fairness, DG2 partner’s hierarchy, their interactions, and the chosen amount of unfair option in each trial by employing a generalized linear model (GLM), using the “glmfit” function in MATLAB.

    Article Title: Keeping time and rhythm by internal simulation of sensory stimuli and behavioral actions.
    Article Snippet: This time- dependent mean firing rate was regressed on the time- dependent stimulus position (arbitrarily left: 0, right: 1) using the Matlab function “glmfit,” with a Poisson “link” model of the firing rate.

    Article Title: Adolescent and adult mice use both incremental reinforcement learning and short term memory when learning concurrent stimulus-action associations.
    Article Snippet: All parameters for within individuals and sessions logistic regressions were estimated using Matlab’s glmfit and fitglm functions or statsmodels package in Python.

    Article Title: Keeping time and rhythm by internal simulation of sensory stimuli and behavioral actions
    Article Snippet: This time-dependent mean firing rate was regressed on the time-dependent stimulus position (arbitrarily left: 0, right: 1) using the Matlab function “glmfit,” with a Poisson “link” model of the firing rate.

    Diagnostic Assay:

    Article Title: Deep learning enables fast and accurate quantification of MRI-guided near-infrared spectral tomography for breast cancer diagnosis
    Article Snippet: The utilization of magnetic resonance (MR) imaging to guide near-infrared spectral tomography (NIRST) shows significant potential for improving the specificity and sensitivity of breast cancer diagnosis.. However, the efficiency and accuracy of NIRST image reconstruction have been limited by the complexities of light propagation modeling and MRI image segmentation.. To address these challenges, we developed and evaluated a deep learning-based approach for MR-guided 3D NIRST image reconstruction (DL-MRg-NIRST).

    Magnetic Resonance Imaging:

    Article Title: Deep learning enables fast and accurate quantification of MRI-guided near-infrared spectral tomography for breast cancer diagnosis
    Article Snippet: The utilization of magnetic resonance (MR) imaging to guide near-infrared spectral tomography (NIRST) shows significant potential for improving the specificity and sensitivity of breast cancer diagnosis.. However, the efficiency and accuracy of NIRST image reconstruction have been limited by the complexities of light propagation modeling and MRI image segmentation.. To address these challenges, we developed and evaluated a deep learning-based approach for MR-guided 3D NIRST image reconstruction (DL-MRg-NIRST).



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    A: Microelectrode recordings were performed in regions of the globus pallidus and thalamus with spike activity that was responsive to passive joint movement. B: Results of experimenter-blinded muscle rigidity scoring for both monkeys at three DBS settings. C and D: Co-registration of pre-operative MRI and post-electrode implantation CT showing DBS electrode location for monkey R (C) and K (D). E and F: Localization of recorded cells obtained from stereotactic navigation software and overlaid on corresponding atlas plates for monkey R (top) and K (bottom) for both the pallidum (E) and the thalamus (F). G: A <t>generalized</t> <t>linear</t> <t>model</t> <t>(GLM)</t> accounting for position, velocity, and acceleration of the joint movement was applied to determine the correlation between kinematics of the joint movement (top row) and spike activity (2 nd row: spike raster, 3 rd row: corresponding rate histogram). Bottom row shows the GLM prediction of firing rate.
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    Image Search Results


    A: Microelectrode recordings were performed in regions of the globus pallidus and thalamus with spike activity that was responsive to passive joint movement. B: Results of experimenter-blinded muscle rigidity scoring for both monkeys at three DBS settings. C and D: Co-registration of pre-operative MRI and post-electrode implantation CT showing DBS electrode location for monkey R (C) and K (D). E and F: Localization of recorded cells obtained from stereotactic navigation software and overlaid on corresponding atlas plates for monkey R (top) and K (bottom) for both the pallidum (E) and the thalamus (F). G: A generalized linear model (GLM) accounting for position, velocity, and acceleration of the joint movement was applied to determine the correlation between kinematics of the joint movement (top row) and spike activity (2 nd row: spike raster, 3 rd row: corresponding rate histogram). Bottom row shows the GLM prediction of firing rate.

    Journal: PLoS ONE

    Article Title: Deep Brain Stimulation Imposes Complex Informational Lesions

    doi: 10.1371/journal.pone.0074462

    Figure Lengend Snippet: A: Microelectrode recordings were performed in regions of the globus pallidus and thalamus with spike activity that was responsive to passive joint movement. B: Results of experimenter-blinded muscle rigidity scoring for both monkeys at three DBS settings. C and D: Co-registration of pre-operative MRI and post-electrode implantation CT showing DBS electrode location for monkey R (C) and K (D). E and F: Localization of recorded cells obtained from stereotactic navigation software and overlaid on corresponding atlas plates for monkey R (top) and K (bottom) for both the pallidum (E) and the thalamus (F). G: A generalized linear model (GLM) accounting for position, velocity, and acceleration of the joint movement was applied to determine the correlation between kinematics of the joint movement (top row) and spike activity (2 nd row: spike raster, 3 rd row: corresponding rate histogram). Bottom row shows the GLM prediction of firing rate.

    Article Snippet: A generalized linear model (GLM) fit function in Matlab (Mathworks, Natick MA) was applied, with Δt=1 ms and covariates of position, velocity, and acceleration in the plane of the tracked limb’s movement ( ).

    Techniques: Activity Assay, Software