# Spatial networks: Gaussian probabilistic kernel¶

Run this example as a Jupyter notebook: JupyterHub service

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 nest
import numpy as np

nest.ResetKernel()
```

create two test layers

```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": nest.spatial_distributions.gaussian(nest.spatial.distance, std=0.5),
}

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,
probability_parameter=cdict["p"],
src_color=color,
tgt_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')
```

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

Gallery generated by Sphinx-Gallery