  The Potjans-Diesmann cortical microcircuit model is a widely used model
originallyimplemented in NEST. Here, we re-implemented the model using NetPyNE,
a high-level Python interface to the NEURON simulator, and reproduced the
findings of theoriginal publication. We also implemented a method for rescaling
the network sizewhich preserves first and second order statistics, building on
existing work on networktheory. The new implementation enables using more
detailed neuron models with mul-ticompartment morphologies and multiple
biophysically realistic channels. This opensthe model to new research,
including the study of dendritic processing, the influenceof individual channel
parameters, and generally multiscale interactions in the network.The rescaling
method provides flexibility to increase or decrease the network size ifrequired
when running these more realistic simulations. Finally, NetPyNE
facilitatesmodifying or extending the model using its declarative language;
optimizing modelparameters; running efficient large-scale parallelized
simulations; and analyzing themodel through built-in methods, including local
field potential calculation and informa-tion flow measures.
