Pynest implementation of a layered model of the neocortical microcircuit.

The implementation is based on a script written by the 'microcircuit model example (Potjans&Diesmann, doi:10.1093/cercor/bhs358)' found in the current nest version.

Adapt parameters in network_params.py sim_params.py

For the simulation, the network parameters are incorporated into a class (model_class.py) which further contains derived parameters and a number of methods related to the mean field approximation of are network under consideration.

simulate_microcircuit.py -- for Potjans' model simulate_transition.py -- for transition from Brunel's to Potjans' model

All data is save to HDF5 files, each run of the simulation creates a new file, with one group for each loop. Spikes and/or membrane potentials are saved according to sim_params.py.

prep_data.py creates a results file, containing (some of) the most important parameters (mean rates, mean membrane potentials, ...). For further analysis, take a look at the corresponding notebooks.

Data is not uploaded.