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.

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')