(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. (A) The conceptual object-based architecture of a biophysically-detailed network of excitatory (blue) and inhibitory (red) cells. Simulating weak PING rhythms using a model specification structure. DynaSim's higher-level specification, described below, easily scales to arbitrarily complex population models and networks of interconnected populations (Figure (Figure4), 4), and does not require significant “boilerplate” code for even very large networks. It facilitates rapid prototyping of neural models by enabling networks of neurons with one or more compartments to be specified by any combination of: (1) equations with conventional mathematical notation (Figures (Figures1, 1, ,2), 2), similar to XPP (Ermentrout, 2002) and the Brian simulator (Goodman and Brette, 2008), (2) built-in MATLAB functions, and (3) predefined, mechanistically-meaningful model objects (Figures (Figures3, 3, ,4), 4), similar to objects in Brian, mechanisms in NEURON (Hines and Carnevale, 1997), and nodes/connections in NEST (Gewaltig and Diesmann, 2007). It enables researchers to focus on model details instead of implementation, while making it easy to share and explore models with the rest of the community. This tool promises to reduce barriers for investigating dynamics in large neural models, facilitate collaborative modeling, and complement other tools being developed in the neuroinformatics community.ĭynaSim ( ) is a MATLAB (MATLAB, 2017) and GNU Octave (Eaton et al., 2016) toolbox developed for rapid prototyping of large neural models and batch simulation management. The design of DynaSim incorporates a novel schema for model specification to facilitate future interoperability with other specifications (e.g., NeuroML, SBML), simulators (e.g., NEURON, Brian, NEST), and web-based applications (e.g., Geppetto) outside MATLAB. The software has been implemented in MATLAB to enable advanced neural modeling using MATLAB, given its popularity and a growing interest in modeling neural systems. It also includes a graphical user interface (DynaSim GUI) that supports full functionality without requiring user programming. DynaSim also includes a large set of features that simplify exploring model dynamics over parameter spaces, running simulations in parallel using both multicore processors and high-performance computer clusters, and analyzing and plotting large numbers of simulated data sets in parallel. The higher-level specification supports arbitrarily complex population models and networks of interconnected populations. Models can be specified by equations directly (similar to XPP or the Brian simulator) or by lists of predefined or custom model components. It is designed to speed up and simplify the process of generating, sharing, and exploring network models of neurons with one or more compartments. MATLAB stores all numeric values as double-precision floating point numbers by default.DynaSim is an open-source MATLAB/GNU Octave toolbox for rapid prototyping of neural models and batch simulation management. Default Numeric Types in MATLAB and Python.This example shows how to create an object from a MATLAB handle When MATLAB functions return output arguments, MATLAB Engine API for Python converts the data into equivalent Python data types. Handle Data Returned from MATLAB to Python.When you pass Python data as input arguments to MATLAB functions, the MATLAB Engine for Python converts the data into equivalent MATLAB data types. Python module provides array classes to represent arrays of MATLAB numeric types as Python variables so that MATLAB arrays can be passed between Python and MATLAB. This example shows how to create a MATLAB array in Python and pass it as the input argument to the MATLAB
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