Gaussian probabilistic kernelΒΆ

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


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,
         '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,

    # 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.axis([-2.0, 2.0, -2.0, 2.0])
    plt.axes().set_aspect('equal', 'box')
    plt.title('Connection targets, Gaussian kernel')

# plt.savefig('gaussex.pdf')

Total running time of the script: ( 0 minutes 0.000 seconds)

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