This is A PREVIEW for NEST 3.0 and NOT an OFFICIAL RELEASE! Some functionality may not be available and information may be incomplete!
pynest microcircuit example¶
Example file to run the microcircuit.
Hendrik Rothe, Hannah Bos, Sacha van Albada; May 2016
The microcirucit model is described Potjans and Diesmann 1. This example uses the function GetNodes, which is deprecated. A deprecation warning is therefore issued. For details about deprecated functions, see documentation.
To run the microcircuit on a local machine, adjust the variables
K_scaling in network_params.py to
N_scaling adjusts the number of neurons and
K_scaling the number
of connections to be simulated. The full network can be run by adjusting
these values to 1. If this is done, the option to print the time
progress should be set to False in the file
python example.py. The output will be saved in the
The code can be parallelized using OpenMP and MPI, if NEST has been
built with these applications (Parallel computing with
NEST). The number
of threads (per MPI process) can be chosen by adjusting
sim_params.py. The number of MPI process
can be set by choosing a reasonable value for
num_mpi_prc and then
running the script with the command
mpirun -n num_mpi_prc
The default version of the simulation uses Poissonian input, which is
defined in the file
network_params.py to excite neuronal populations
of the microcircuit. If no Poissonian input is provided, DC input is
calculated which should approximately compensate the Poissonian input.
It is also possible to add thalamic stimulation to the microcircuit or
drive it with constant DC input. This can be defined in the file
Potjans TC. and Diesmann M. 2014. The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model. Cerebral Cortex. 24(3):785–806. DOI: 10.1093/cercor/bhs358.
import time import numpy as np import network from network_params import net_dict from sim_params import sim_dict from stimulus_params import stim_dict # Initialize the network and pass parameters to it. tic = time.time() net = network.Network(sim_dict, net_dict, stim_dict) toc = time.time() - tic print("Time to initialize the network: %.2f s" % toc) # Connect all nodes. tic = time.time() net.setup() toc = time.time() - tic print("Time to create the connections: %.2f s" % toc) # Simulate. tic = time.time() net.simulate() toc = time.time() - tic print("Time to simulate: %.2f s" % toc) # Plot a raster plot of the spikes of the simulated neurons and the average # spike rate of all populations. For visual purposes only spikes 100 ms # before and 100 ms after the thalamic stimulus time are plotted here by # default. The computation of spike rates discards the first 500 ms of # the simulation to exclude initialization artifacts. raster_plot_time_idx = np.array( [stim_dict['th_start'] - 100.0, stim_dict['th_start'] + 100.0] ) fire_rate_time_idx = np.array([500.0, sim_dict['t_sim']]) net.evaluate(raster_plot_time_idx, fire_rate_time_idx)