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
Create two populations on a 30x30 grid and connect them using a Gaussian probabilistic kernel¶
BCCN Tutorial @ CNS*09 Hans Ekkehard Plesser, UMB
import matplotlib.pyplot as plt
import numpy as np
import nest
nest.ResetKernel()
create two test layers
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': nest.spatial_distributions.gaussian(nest.spatial.distance,
std=0.5),
'mask': {'circular': {'radius': 3.0}}}
nest.Connect(a, b, cdict)
plot targets of neurons in different grid locations
plot targets of two source neurons into same figure, with mask use different colors
for src_index, color, cmap in [(30 * 15 + 15, 'blue', 'Blues'), (0, 'green', 'Greens')]:
# obtain node id for center
src = a[src_index:src_index + 1]
fig = plt.figure()
nest.PlotTargets(src, b, mask=cdict['mask'], probability_parameter=cdict['p'],
src_color=color, tgt_color=color, mask_color=color,
probability_cmap=cmap, src_size=100,
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, Gaussian kernel')
plt.show()
# plt.savefig('gaussex.pdf')
Total running time of the script: ( 0 minutes 0.000 seconds)