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# 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

nest.ResetKernel()
```

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,
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()
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)

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