The project 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, proposed by Mejias et. al, was successfully converted. A natural extension of this model was proposed in a paper by Joglekar et. al . 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. My goal in this project is to convert this model to PyNN allowing the simulation in several simulators. As a secondary goal in this project, I would like to convert the model proposed by Demirtas et. al. 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.