1This work targets the replicability of computational models to provide the community with tested and proven simulator and obtain the same results presented in the reference article. We did not replicate analyses that 7 involve changes in the network structure. Our replicated network model presents activity dynamic patterns very 8 similar to the ones observed in the original model, with comparisons made in terms of firing rates and synchrony 9 and irregularity measures. In conclusion, the Potjans-Diesmann model was successfully replicated in a different 10 platform than the one in which it was originally implemented. Most theoretical studies of cortical activity are based on networks of randomly connected units [2,6,7,12] or 13 with architectures artificially built from random networks [10]. In spite of the usefulness of these models, in 14 order to understand the interplay between network structure and cortical dynamics it is essential to have 15 computational models which accurately represent the cortical network architecture. Recently, Potjans and 16 Diesmann [8] developed a network model of the local cortical microcircuit based on extensive experimental data 17 on the intrinsic circuitry of striate cortex [1,9]. The model contains two cell types (excitatory and inhibitory) 18 distributed over four layers, L2/3, L4, L5, and L6, and represents the cortical network below a surface area of 1 19 mm 2 (a scheme is shown in Fig. 1). 20The original implementation was based on the NEST simulator [4] and the source code is available at the Methods 24In this work, we replicated in Brian 2 every detail of the Potjans-Diesmann model as described in their original 25 article [8]. Hereafter, we will refer to the original NEST implementation of the Potjans-Diesmann model [8] as 26 reference (or original) article. In this section we explain how this reimplementation was done. Further statistical 27 analyses were performed using SciPy, NumPy, and Matplotlib libraries for the Python language.