Spatial networks: Gaussian probabilistic kernel

---

Run this example as a Jupyter notebook:

JupyterHub service

See our guide for more information and troubleshooting.

---

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),
    "mask": {"circular": {"radius": 3.0}},
}

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,
        mask=cdict["mask"],
        probability_parameter=cdict["p"],
        src_color=color,
        tgt_color=color,
        mask_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')