Warning
This version of the documentation is NOT an official release. You are looking at ‘latest’, which is in active and ongoing development. You can change versions on the bottom left of the screen.
Note
Click here to download the full example code
Connect with circular mask, flat probability using 2 populations of iaf_psc_alpha neurons¶
Create two populations on a 30x30 grid of iaf_psc_alpha neurons, connect with circular mask, flat probability, visualize.
BCCN Tutorial @ CNS*09 Hans Ekkehard Plesser, UMB
import nest
import matplotlib.pyplot as plt
import numpy as np
nest.ResetKernel()
pos = nest.spatial.grid(shape=[30, 30], extent=[3., 3.])
create and connect two populations
a = nest.Create('iaf_psc_alpha', positions=pos)
b = nest.Create('iaf_psc_alpha', positions=pos)
cdict = {'rule': 'pairwise_bernoulli',
'p': 0.5,
'mask': {'circular': {'radius': 0.5}}}
nest.Connect(a, b,
conn_spec=cdict,
syn_spec={'weight': nest.random.uniform(0.5, 2.)})
plot targets of neurons in different grid locations
# first, clear existing figure, get current figure
plt.clf()
fig = plt.gcf()
# plot targets of two source neurons into same figure, with mask
for src_index in [30 * 15 + 15, 0]:
# obtain node id for center
src = a[src_index:src_index + 1]
nest.PlotTargets(src, b, mask=cdict['mask'], fig=fig)
# beautify
plt.axes().set_xticks(np.arange(-1.5, 1.55, 0.5))
plt.axes().set_yticks(np.arange(-1.5, 1.55, 0.5))
plt.grid(True)
plt.axis([-2.0, 2.0, -2.0, 2.0])
plt.axes().set_aspect('equal', 'box')
plt.title('Connection targets')
plt.show()
# plt.savefig('connex.pdf')
Total running time of the script: ( 0 minutes 0.000 seconds)