Models that contain the Modeling Application : NeuroML (Home Page)

( "Model Descriptions for Computational Neuroscience. Computational models based on detailed neuroanatomical and electrophysiological data have been used for many years as an aid for understanding the function of the nervous system. NeuroML is an international, collaborative initiative to develop a language for describing detailed models of neural systems. The aims of the NeuroML initiative are: To create specifications for a language in XML to describe the biophysics, anatomy and network architecture of neuronal systems at multiple scales To facilitate the exchange of complex neuronal models between researchers, allowing for greater transparency and accessibility of models To promote software tools which support NeuroML and support the development of new software and databases To encourage researchers with models within the scope of NeuroML to exchange and publish their models in this format. NeuroML is a free and open community effort developed with input from many contributors. We need your help as the language and tools continue to evolve. ... ")
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    Models   Description
1.  KInNeSS : a modular framework for computational neuroscience (Versace et al. 2008)
The xml files provided here implement a network of excitatory and inhibitory spiking neurons, governed by either Hodgkin-Huxley or quadratic integrate-and-fire dynamical equations. The code is used to demonstrate the capabilities of the KInNeSS software package for simulation of networks of spiking neurons. The simulation protocol used here is meant to facilitate the comparison of KInNeSS with other simulators reviewed in <a href="">Brette et al. (2007)</a>. See the associated paper "Versace et al. (2008) KInNeSS : a modular framework for computational neuroscience." for an extensive description of KInNeSS .
2.  Layer V pyramidal cell model with reduced morphology (Mäki-Marttunen et al 2018)
" ... In this work, we develop and apply an automated, stepwise method for fitting a neuron model to data with fine spatial resolution, such as that achievable with voltage sensitive dyes (VSDs) and Ca2+ imaging. ... We apply our method to simulated data from layer 5 pyramidal cells (L5PCs) and construct a model with reduced neuronal morphology. We connect the reduced-morphology neurons into a network and validate against simulated data from a high-resolution L5PC network model. ..."
3.  NEURON + Python (Hines et al. 2009)
The NEURON simulation program now allows Python to be used alone or in combination with NEURON's traditional Hoc interpreter. Adding Python to NEURON has the immediate benefit of making available a very extensive suite of analysis tools written for engineering and science. It also catalyzes NEURON software development by offering users a modern programming tool that is recognized for its flexibility and power to create and maintain complex programs. At the same time, nothing is lost because all existing models written in Hoc, including GUI tools, continue to work without change and are also available within the Python context. An example of the benefits of Python availability is the use of the xml module in implementing NEURON's Import3D and CellBuild tools to read MorphML and NeuroML model specifications.

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