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A great advance in the digital reconstruction of brain microcircuits came with the model of the primary somatosensory cortex (S1) of rats developed by the Blue Brain Project in 2015. In this microcircuit, each column had around 31,000 neurons, 55 layer-specific morphological population, and 207 morpho-electrical neuron sub-types. The complex network of S1 included around 8 million connections with 37 million synapses. Here, we implemented a version of the S1 model using NetPyNE, a high-level Python interface to the NEURON simulator (Fig.1). First, we downloaded all data available in The Neocortical Microcircuit Collaboration Portal ( ). Secondly, we imported the 1035 reconstructed cells to NetPyNE and tested the somatic membrane potential under different current clamp amplitudes. Later, using the connectoma of 7 neocortical columns, we obtained the connection probability rules of the 1941m-type pathways. The connection probability between two neurons depends on the distance between them, but we note that, in most cases, two different fits are required to describe these probability rules. The long-range connections are well fitted by an exponential decay, but for short range (<100 um) the connections are well represented by using a linear fit rule. We reconstructed the S1 in NetPyNE distributing the 31,346 cells within a cylindrical volume with 2082 um height and radius of 210 um, where each sub-type was randomly distributed in its specific layer (L1, L2/3, L4, L5, or L6). Then, we created the network with synaptic transmission parameters for each pathway and added spontaneous synaptic release as a Poisson stimulus. Finally, we simulated the model and explored the spontaneous rates for excitatory and inhibitory synapses in order to find biologically constrained values for neuronal firing rates.

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