Conversion of large scale cortical models into PyNN/NeuroML
Ronaldo Valter Nunes
The aim is conversion of large scale cortical models into PyNN/NeuroML involves the conversion of published large scale network models into open, simulator independent and testing them across multiple simulator implementations.
Conversion of large scale cortical models into PyNN/NeuroML involves the conversion of published large scale network models into open, simulator independent and testing them across multiple simulator implementations. In the previous edition of GSOC the large scale network model for the macaque cortex (https://github.com/OpenSourceBrain/MejiasEtAl2016), proposed by Mejias et. al, was successfully converted. In this model, each cortical area is composed of an inferior and a superior layer and the dynamical behavior inside each laminar subcircuit is described by a non-linear firing rate model of Wilson-Cowan type which represents the mean activities of a population of excitatory neurons and a population of inhibitory neurons. A natural extension of this model was proposed in a paper by Joglekar et. al (https://www.ncbi.nlm.nih.gov/pubmed/29576389). Instead of using non-linear firing rate models, the cortical area was simulated as a spiking neuronal network. This was extremely useful to investigate the propagation of activity in the synchronous and asynchronous regime of the network. Although this study was published in 2018, the code is not available in ModelDB. However, it was written in Brian simulator and can be kindly provided by the authors. My goal in this project is to convert this model to PyNN allowing the simulation in several simulators. Besides that, with the firing rate large scale model previously converted it will make possible the full reproducibility of the results published in the paper (https://www.ncbi.nlm.nih.gov/pubmed/29576389). As a secondary goal in this project, I would like to convert the model proposed by Demirtas et. al. (https://doi.org/10.1016/j.neuron.2019.01.017) that is a large-scale circuit model of human cortex incorporating regional heterogeneity in microcircuit properties inferred from magnetic resonance imaging (MRI) for parametrization across the cortical hierarchy and fitting models to resting-state functional connectivity.