Warning

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 N_scaling and K_scaling in network_params.py to 0.1. 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 sim_params.py. For running, use python example.py. The output will be saved in the directory data.

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 local_num_threads in 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 python example.py.

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 stimulus_params.py.

## References¶

1

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)