Spatial networks: Circular mask and flat probabilityΒΆ

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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 matplotlib.pyplot as plt
import nest
import numpy as np


pos = nest.spatial.grid(shape=[30, 30], extent=[3.0, 3.0])

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, 2.0)})

plot targets of neurons in different grid locations

# first, clear existing figure, get current figure
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.axis([-2.0, 2.0, -2.0, 2.0])
plt.axes().set_aspect("equal", "box")
plt.title("Connection targets")

# plt.savefig('connex.pdf')

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