Spatial networks: Convergent projection and rectangular mask, from source perspective

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Create two populations of iaf_psc_alpha neurons on a 30x30 grid Connect the two populations with convergent projection and rectangular mask, and visualize connections from source perspective

BCCN Tutorial @ CNS*09 Hans Ekkehard Plesser, UMB

import matplotlib.pyplot as plt
import nest
import numpy as np

nest.ResetKernel()

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

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,
    "use_on_source": True,
    "mask": {"rectangular": {"lower_left": [-0.2, -0.5], "upper_right": [0.2, 0.5]}},
}

nest.Connect(a, b, conn_spec=cdict, syn_spec={"weight": nest.random.uniform(0.5, 2.0)})

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('conncon_targets.pdf')