How to use the nml.PGGSynapse function in nml
To help you get started, we’ve selected a few nml examples, based on popular ways it is used in public projects.
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neurokernel / neurokernel / neurokernel / neuroml / utils.py View on Github 
Vt=attr_dict['Vt'],
R=attr_dict['R'],
C=attr_dict['C'])
module.lif_neurons.append(lif)
for s in g.edges():
attr_dict = g.edge[s[0]][s[1]]
if attr_dict['model'] == 'AlphaSynapse':
al = AlSynapse(id=attr_dict['name'],
class_=attr_dict['class'],
ar=attr_dict['ar'],
ad=attr_dict['ad'],
gmax=attr_dict['gmax'],
reverse=attr_dict['reverse'])
module.al_synapses.append(al)
elif attr_dict['model'] == 'power_gpot_gpot':
pgg = PGGSynapse(id=attr_dict['name'],
class_=attr_dict['class'],
slope=attr_dict['slope'],
threshold=attr_dict['threshold'],
power=attr_dict['power'],
saturation=attr_dict['saturation'],
delay=attr_dict['delay'],
reverse=attr_dict['reverse'],
conductance=attr_dict['conductance'])
module.pgg_synapses.append(pgg)
interface = Interface()
for p in i.nodes():
attr_dict = i.node[p]
port = Port(identifier=p,
interface=attr_dict['interface'],
io=attr_dict['io'],nml
An OpenTTD NewGRF compiler for the nml language
