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Simulating a simple system of ordinary differential equations in <t>DynaSim.</t> (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.

Simulating a simple system of ordinary differential equations in <t>DynaSim.</t> (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.

Dynasim Gui, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 95/100, based on 163 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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dynasim gui - by Bioz Stars, 2026-04

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1) Product Images from "DynaSim: A MATLAB Toolbox for Neural Modeling and Simulation"

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

Journal: Frontiers in Neuroinformatics

doi: 10.3389/fninf.2018.00010

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.

Figure Legend 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.

Techniques Used:

Simulating an ODE system with conditional reset and stochastic drive. (A) MATLAB code using the DynaSim toolbox. The model is specified using a cell array of strings, eqns , listing equations defining parameters, an input function I(t) , ODEs with ICs, and a conditional reset. The stochastic input uses the built-in MATLAB function rand . (B) Plot of the time-varying input and simulated output.

Figure Legend Snippet: Simulating an ODE system with conditional reset and stochastic drive. (A) MATLAB code using the DynaSim toolbox. The model is specified using a cell array of strings, eqns , listing equations defining parameters, an input function I(t) , ODEs with ICs, and a conditional reset. The stochastic input uses the built-in MATLAB function rand . (B) Plot of the time-varying input and simulated output.

Techniques Used:

Simulating a biophysically-detailed neuron model using mechanisms. (A) DynaSim model leveraging existing model objects for iNaF, iKDR, and iM currents to simplify the specification of a detailed neuron model. (B) IB response to tonic current.

Figure Legend Snippet: Simulating a biophysically-detailed neuron model using mechanisms. (A) DynaSim model leveraging existing model objects for iNaF, iKDR, and iM currents to simplify the specification of a detailed neuron model. (B) IB response to tonic current.

Techniques Used:

Simulating weak PING rhythms using a model specification structure. (A) The conceptual object-based architecture of a biophysically-detailed network of excitatory (blue) and inhibitory (red) cells. (B) Mapping the object-based architecture onto a DynaSim specification structure that contains all the high-level information necessary to construct the complete system of equations for the full model using objects from a library of pre-existing ionic mechanisms.

Figure Legend Snippet: Simulating weak PING rhythms using a model specification structure. (A) The conceptual object-based architecture of a biophysically-detailed network of excitatory (blue) and inhibitory (red) cells. (B) Mapping the object-based architecture onto a DynaSim specification structure that contains all the high-level information necessary to construct the complete system of equations for the full model using objects from a library of pre-existing ionic mechanisms.

Techniques Used: Construct

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.

Figure Legend 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.

Techniques Used: Produced

Figure Legend Snippet: DynaSim Graphical User Interface showing the weak PING model.

Techniques Used:

Object-based architecture, standardized specification, and DynaSim models. (A) Object-based architecture and standardized specification. Discrete model objects (populations and mechanisms) are shown in bold; any object can be stored independently in the library and reused as components of larger models. There is no limit on the number of objects in a DynaSim model. Fields of the standardized specification structure are underlined. Each population can have a list of intrinsic mechanisms; each directed pair of source and target populations can have a list of connection mechanisms. Optional objects are enclosed in parentheses. A string-based specification will be internally associated with a default population “pop1” in the standardized specification structure. (B) The standardized specification structure and model objects are parsed to generate a single set of equations describing the full model given the separate sets of equations for each object.

Figure Legend Snippet: Object-based architecture, standardized specification, and DynaSim models. (A) Object-based architecture and standardized specification. Discrete model objects (populations and mechanisms) are shown in bold; any object can be stored independently in the library and reused as components of larger models. There is no limit on the number of objects in a DynaSim model. Fields of the standardized specification structure are underlined. Each population can have a list of intrinsic mechanisms; each directed pair of source and target populations can have a list of connection mechanisms. Optional objects are enclosed in parentheses. A string-based specification will be internally associated with a default population “pop1” in the standardized specification structure. (B) The standardized specification structure and model objects are parsed to generate a single set of equations describing the full model given the separate sets of equations for each object.

Techniques Used:

Linking equations across population and mechanism objects. Mechanism linker statements with addition assignment (e.g., @current+=IK) direct DynaSim to substitute functions INa and IK into population-level dynamics “dv/dt,” where the linker appears (i.e., @current). In this example, intrinsic mechanisms are defined in script and added to specification structure in a mechanisms field.

Figure Legend Snippet: Linking equations across population and mechanism objects. Mechanism linker statements with addition assignment (e.g., @current+=IK) direct DynaSim to substitute functions INa and IK into population-level dynamics “dv/dt,” where the linker appears (i.e., @current). In this example, intrinsic mechanisms are defined in script and added to specification structure in a mechanisms field.

Techniques Used:

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.

Figure Legend 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.

Techniques Used: Functional Assay, Generated

Benchmarks. Time to simulate vs. network size for all benchmarks run; network sizes were 1, 2, 4, 8, 16, 32, 64, 128, 250, 500, 1,000, 2,000, 4,000, 8,000, 16,000, or 32,000 cells. Red lines indicate uncompiled Brian 2 simulation time for given network type and size, green lines indicate time for equivalent C++ compiled Brian 2 simulation, blue lines indicate time for equivalent DynaSim simulation without using MEX compilation, and black lines indicate time for equivalent DynaSim simulation using MEX compilation. (A) Benchmarks for simple “current-based” (CUBA) simulations consisting of cells containing just leakage currents and no synapses. (B) Benchmarks for Hodgkin-Huxley conductance-based (COBAHH) simulations of cells containing Na, K, and leakage currents and no synapses. (C) Benchmarks for COBAHH simulations, but with AMPA and GABA-A synaptic connections at a low density of 2% connection probability. (D) Benchmarks for COBAHH simulations, but with AMPA and GABA-A synaptic connections at a high density of 90% connection probability. Note that we could not simulate the highest-sized network (32,000 cells) using compilation under DynaSim, as the resulting data structures were found to be too large to be computed by MATLAB's compiling framework. DynaSim simulations using compilation worked successfully using network sizes of 16,000 cells, and those without compilation could successfully simulate 32,000 cells.

Figure Legend Snippet: Benchmarks. Time to simulate vs. network size for all benchmarks run; network sizes were 1, 2, 4, 8, 16, 32, 64, 128, 250, 500, 1,000, 2,000, 4,000, 8,000, 16,000, or 32,000 cells. Red lines indicate uncompiled Brian 2 simulation time for given network type and size, green lines indicate time for equivalent C++ compiled Brian 2 simulation, blue lines indicate time for equivalent DynaSim simulation without using MEX compilation, and black lines indicate time for equivalent DynaSim simulation using MEX compilation. (A) Benchmarks for simple “current-based” (CUBA) simulations consisting of cells containing just leakage currents and no synapses. (B) Benchmarks for Hodgkin-Huxley conductance-based (COBAHH) simulations of cells containing Na, K, and leakage currents and no synapses. (C) Benchmarks for COBAHH simulations, but with AMPA and GABA-A synaptic connections at a low density of 2% connection probability. (D) Benchmarks for COBAHH simulations, but with AMPA and GABA-A synaptic connections at a high density of 90% connection probability. Note that we could not simulate the highest-sized network (32,000 cells) using compilation under DynaSim, as the resulting data structures were found to be too large to be computed by MATLAB's compiling framework. DynaSim simulations using compilation worked successfully using network sizes of 16,000 cells, and those without compilation could successfully simulate 32,000 cells.

Techniques Used:

Image Search Results

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: Several features are not currently supported by GNU Octave including the DynaSim GUI, MATLAB Coder for MEX compilation, and parallel simulations using parfor .

Techniques:

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 an ODE system with conditional reset and stochastic drive. (A) MATLAB code using the DynaSim toolbox. The model is specified using a cell array of strings, eqns , listing equations defining parameters, an input function I(t) , ODEs with ICs, and a conditional reset. The stochastic input uses the built-in MATLAB function rand . (B) Plot of the time-varying input and simulated output.

Article Snippet: Several features are not currently supported by GNU Octave including the DynaSim GUI, MATLAB Coder for MEX compilation, and parallel simulations using parfor .

Techniques:

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 biophysically-detailed neuron model using mechanisms. (A) DynaSim model leveraging existing model objects for iNaF, iKDR, and iM currents to simplify the specification of a detailed neuron model. (B) IB response to tonic current.

Article Snippet: Several features are not currently supported by GNU Octave including the DynaSim GUI, MATLAB Coder for MEX compilation, and parallel simulations using parfor .

Techniques:

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 weak PING rhythms using a model specification structure. (A) The conceptual object-based architecture of a biophysically-detailed network of excitatory (blue) and inhibitory (red) cells. (B) Mapping the object-based architecture onto a DynaSim specification structure that contains all the high-level information necessary to construct the complete system of equations for the full model using objects from a library of pre-existing ionic mechanisms.

Article Snippet: Several features are not currently supported by GNU Octave including the DynaSim GUI, MATLAB Coder for MEX compilation, and parallel simulations using parfor .

Techniques: Construct

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: Several features are not currently supported by GNU Octave including the DynaSim GUI, MATLAB Coder for MEX compilation, and parallel simulations using parfor .

Techniques: Produced

Journal: Frontiers in Neuroinformatics

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

doi: 10.3389/fninf.2018.00010

Figure Lengend Snippet: DynaSim Graphical User Interface showing the weak PING model.

Article Snippet: Several features are not currently supported by GNU Octave including the DynaSim GUI, MATLAB Coder for MEX compilation, and parallel simulations using parfor .

Techniques:

Journal: Frontiers in Neuroinformatics

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

doi: 10.3389/fninf.2018.00010

Figure Lengend Snippet: Object-based architecture, standardized specification, and DynaSim models. (A) Object-based architecture and standardized specification. Discrete model objects (populations and mechanisms) are shown in bold; any object can be stored independently in the library and reused as components of larger models. There is no limit on the number of objects in a DynaSim model. Fields of the standardized specification structure are underlined. Each population can have a list of intrinsic mechanisms; each directed pair of source and target populations can have a list of connection mechanisms. Optional objects are enclosed in parentheses. A string-based specification will be internally associated with a default population “pop1” in the standardized specification structure. (B) The standardized specification structure and model objects are parsed to generate a single set of equations describing the full model given the separate sets of equations for each object.

Article Snippet: Several features are not currently supported by GNU Octave including the DynaSim GUI, MATLAB Coder for MEX compilation, and parallel simulations using parfor .

Techniques:

Journal: Frontiers in Neuroinformatics

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

doi: 10.3389/fninf.2018.00010

Figure Lengend Snippet: Linking equations across population and mechanism objects. Mechanism linker statements with addition assignment (e.g., @current+=IK) direct DynaSim to substitute functions INa and IK into population-level dynamics “dv/dt,” where the linker appears (i.e., @current). In this example, intrinsic mechanisms are defined in script and added to specification structure in a mechanisms field.

Article Snippet: Several features are not currently supported by GNU Octave including the DynaSim GUI, MATLAB Coder for MEX compilation, and parallel simulations using parfor .

Techniques:

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: Several features are not currently supported by GNU Octave including the DynaSim GUI, MATLAB Coder for MEX compilation, and parallel simulations using parfor .

Techniques: Functional Assay, Generated

Journal: Frontiers in Neuroinformatics

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

doi: 10.3389/fninf.2018.00010

Figure Lengend Snippet: Benchmarks. Time to simulate vs. network size for all benchmarks run; network sizes were 1, 2, 4, 8, 16, 32, 64, 128, 250, 500, 1,000, 2,000, 4,000, 8,000, 16,000, or 32,000 cells. Red lines indicate uncompiled Brian 2 simulation time for given network type and size, green lines indicate time for equivalent C++ compiled Brian 2 simulation, blue lines indicate time for equivalent DynaSim simulation without using MEX compilation, and black lines indicate time for equivalent DynaSim simulation using MEX compilation. (A) Benchmarks for simple “current-based” (CUBA) simulations consisting of cells containing just leakage currents and no synapses. (B) Benchmarks for Hodgkin-Huxley conductance-based (COBAHH) simulations of cells containing Na, K, and leakage currents and no synapses. (C) Benchmarks for COBAHH simulations, but with AMPA and GABA-A synaptic connections at a low density of 2% connection probability. (D) Benchmarks for COBAHH simulations, but with AMPA and GABA-A synaptic connections at a high density of 90% connection probability. Note that we could not simulate the highest-sized network (32,000 cells) using compilation under DynaSim, as the resulting data structures were found to be too large to be computed by MATLAB's compiling framework. DynaSim simulations using compilation worked successfully using network sizes of 16,000 cells, and those without compilation could successfully simulate 32,000 cells.

Article Snippet: Several features are not currently supported by GNU Octave including the DynaSim GUI, MATLAB Coder for MEX compilation, and parallel simulations using parfor .

Techniques:

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