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matlab compiler called mex  (MathWorks Inc)


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    MathWorks Inc matlab compiler called mex
    Matlab Compiler Called Mex, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 297 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/matlab compiler called mex/product/MathWorks Inc
    Average 96 stars, based on 297 article reviews
    matlab compiler called mex - by Bioz Stars, 2026-03
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    Simulating a simple system of ordinary differential equations in DynaSim. (A) MATLAB code using the DynaSim toolbox. Simulation is achieved by passing a model specification to the DynaSim dsSimulate function. Simulated data are returned in a DynaSim data structure. (B) (x,z) phase plane of Lorenz system.

    Journal: Frontiers in Neuroinformatics

    Article Title: DynaSim: A MATLAB Toolbox for Neural Modeling and Simulation

    doi: 10.3389/fninf.2018.00010

    Figure Lengend Snippet: Simulating a simple system of ordinary differential equations in DynaSim. (A) MATLAB code using the DynaSim toolbox. Simulation is achieved by passing a model specification to the DynaSim dsSimulate function. Simulated data are returned in a DynaSim data structure. (B) (x,z) phase plane of Lorenz system.

    Article Snippet: The benchmarks described below show that the speed of most simulations can be increased by up to a factor of 10x by setting the compile_flag option to 1: which directs dsSimulate to compile the simulation into C code (i.e., MATLAB MEX compilation) before numerical integration.

    Techniques:

    Searching parameter space using the DynaSim toolbox. (A) MATLAB code using the DynaSim dsSimulate function with the vary option to specify a set of 9 simulations varying two parameters ( Iapp in population E and tauD of the connection from I to E). (B) Raster plots produced by dsPlot with the plot_type option given an array of DynaSim data structures containing results for all 9 simulations. (C) Plots produced by dsPlotFR showing how mean firing rates for E and I populations change as a function of the two varied parameters.

    Journal: Frontiers in Neuroinformatics

    Article Title: DynaSim: A MATLAB Toolbox for Neural Modeling and Simulation

    doi: 10.3389/fninf.2018.00010

    Figure Lengend Snippet: Searching parameter space using the DynaSim toolbox. (A) MATLAB code using the DynaSim dsSimulate function with the vary option to specify a set of 9 simulations varying two parameters ( Iapp in population E and tauD of the connection from I to E). (B) Raster plots produced by dsPlot with the plot_type option given an array of DynaSim data structures containing results for all 9 simulations. (C) Plots produced by dsPlotFR showing how mean firing rates for E and I populations change as a function of the two varied parameters.

    Article Snippet: The benchmarks described below show that the speed of most simulations can be increased by up to a factor of 10x by setting the compile_flag option to 1: which directs dsSimulate to compile the simulation into C code (i.e., MATLAB MEX compilation) before numerical integration.

    Techniques: Produced

    Single simulation workflow. From the user perspective, the functional interface to DynaSim involves specifying a model using strings or a DynaSim specification structure, passing it to dsSimulate , and obtaining a DynaSim data structure with the results of simulation. Internally, dsSimulate standardizes the supplied specification using the dsCheckSpecification function. The standardized specification structure is converted into a DynaSim model structure (Figure ) using the dsGenerateModel function, which adds object-specific namespace identifiers and links variables and functions across model objects (Figure ). A solve_file for numerical integration is automatically generated from the model structure by dsGetSolveFile according to simulator options. Simulated data is then obtained by evaluating the solve_file . DynaSim structures are shown in bold. Functions are followed by “().” Simulator options are enclosed in parentheses.

    Journal: Frontiers in Neuroinformatics

    Article Title: DynaSim: A MATLAB Toolbox for Neural Modeling and Simulation

    doi: 10.3389/fninf.2018.00010

    Figure Lengend Snippet: Single simulation workflow. From the user perspective, the functional interface to DynaSim involves specifying a model using strings or a DynaSim specification structure, passing it to dsSimulate , and obtaining a DynaSim data structure with the results of simulation. Internally, dsSimulate standardizes the supplied specification using the dsCheckSpecification function. The standardized specification structure is converted into a DynaSim model structure (Figure ) using the dsGenerateModel function, which adds object-specific namespace identifiers and links variables and functions across model objects (Figure ). A solve_file for numerical integration is automatically generated from the model structure by dsGetSolveFile according to simulator options. Simulated data is then obtained by evaluating the solve_file . DynaSim structures are shown in bold. Functions are followed by “().” Simulator options are enclosed in parentheses.

    Article Snippet: The benchmarks described below show that the speed of most simulations can be increased by up to a factor of 10x by setting the compile_flag option to 1: which directs dsSimulate to compile the simulation into C code (i.e., MATLAB MEX compilation) before numerical integration.

    Techniques: Functional Assay, Generated