Create two 30x30 layers of iaf_psc_alpha neurons (circular mask)

Connect with circular mask, flat probability, visualize.

BCCN Tutorial @ CNS*09 Hans Ekkehard Plesser, UMB

import nest
import nest.topology as topo
import pylab

pylab.ion()


nest.ResetKernel()

# create two test layers
a = topo.CreateLayer({'columns': 30, 'rows': 30, 'extent': [3.0, 3.0],
                      'elements': 'iaf_psc_alpha'})
b = topo.CreateLayer({'columns': 30, 'rows': 30, 'extent': [3.0, 3.0],
                      'elements': 'iaf_psc_alpha'})

conndict = {'connection_type': 'divergent',
            'mask': {'circular': {'radius': 0.5}},
            'kernel': 0.5,
            'weights': {'uniform': {'min': 0.5, 'max': 2.0}},
            'delays': 1.0}
topo.ConnectLayers(a, b, conndict)

# plot targets of neurons in different grid locations

# first, clear existing figure, get current figure
pylab.clf()
fig = pylab.gcf()

# plot targets of two source neurons into same figure, with mask
for src_pos in [[15, 15], [0, 0]]:
    # obtain node id for center
    src = topo.GetElement(a, src_pos)
    topo.PlotTargets(src, b, mask=conndict['mask'], fig=fig)

# beautify
pylab.axes().set_xticks(pylab.arange(-1.5, 1.55, 0.5))
pylab.axes().set_yticks(pylab.arange(-1.5, 1.55, 0.5))
pylab.grid(True)
pylab.axis([-2.0, 2.0, -2.0, 2.0])
pylab.axes().set_aspect('equal', 'box')
pylab.title('Connection targets')

# pylab.savefig('connex.pdf')

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