# Spatial networks: Convergent projection and rectangular mask, from target perspectiveΒΆ

Run this example as a Jupyter notebook:

Create two populations of iaf_psc_alpha neurons on a 30x30 grid

Connect the two populations with convergent projection and rectangular mask and visualize connection from target perspective.

BCCN Tutorial @ CNS*09 Hans Ekkehard Plesser, UMB

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

nest.ResetKernel()
nest.set_verbosity("M_WARNING")

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)

nest.Connect(
a,
b,
conn_spec={
"rule": "pairwise_bernoulli",
"p": 0.5,
"use_on_source": True,
"mask": {"rectangular": {"lower_left": [-0.2, -0.5], "upper_right": [0.2, 0.5]}},
},
syn_spec={"weight": nest.random.uniform(0.5, 2.0)},
)
plt.clf()
```

plot sources of neurons in different grid locations

```for tgt_index in [30 * 15 + 15, 0]:
# obtain node id for center
tgt = a[tgt_index : tgt_index + 1]

# obtain list of outgoing connections for ctr
spos = nest.GetTargetPositions(tgt, b)[0]

spos_x = np.array([x for x, y in spos])
spos_y = np.array([y for x, y in spos])

print(spos_x)
print(spos_y)

# scatter-plot
plt.scatter(spos_x, spos_y, 20, zorder=10)

# mark sender position with transparent red circle
ctrpos = np.array(nest.GetPosition(tgt))

# mark mask position with open red rectangle
plt.gca().add_patch(plt.Rectangle(ctrpos - (0.2, 0.5), 0.4, 1.0, zorder=1, fc="none", ec="r", lw=3))

# mark layer edge
plt.gca().add_patch(plt.Rectangle((-1.5, -1.5), 3.0, 3.0, zorder=1, fc="none", ec="k", lw=3))

# 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 sources")
plt.show()
```

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