net.projections.append(Projection(id='projEI',
presynaptic=pE.id,
postsynaptic=pRS.id,
synapse='ampa',
delay=2,
weight=0.2,
random_connectivity=RandomConnectivity(probability=.8)))
"""
net.inputs.append(
Input(
id="stim0",
input_source=input_source_p0.id,
population=pop0.id,
percentage=50,
weight="weightInput",
))
net.inputs.append(
Input(id="stim1",
input_source=input_source1.id,
population=pop1.id,
percentage=100))
net.inputs.append(
Input(id="stim2",
input_source=input_source1.id,
population=pop2.id,
percentage=50))
net.synapses.append(
Synapse(id='gaba', neuroml2_source_file='test_files/gaba.synapse.nml'))
net.projections.append(
Projection(id='projEI',
presynaptic=pE.id,
postsynaptic=pRS.id,
synapse='ampa',
delay=2,
weight=0.2,
random_connectivity=RandomConnectivity(probability=.8)))
net.inputs.append(
Input(id='stim',
input_source=input_source.id,
population=pE.id,
percentage=50,
weight='weightInput'))
print(net)
print(net.to_json())
new_file = net.to_json_file('%s.json' % net.id)
################################################################################
### Build Simulation object & save as JSON
sim = Simulation(id='SimExample8',
network=new_file,
duration='1000',
dt='0.025',
recordTraces={'all': '*'})
net.projections[0].random_connectivity = RandomConnectivity(probability=1)
net.projections.append(
Projection(
id="proj1",
presynaptic=p0.id,
postsynaptic=p1.id,
synapse=nmdaSyn.id,
delay=0,
weight="weight",
))
net.projections[1].random_connectivity = RandomConnectivity(probability=1)
net.inputs.append(
Input(id="stim",
input_source=input_source.id,
population=p0.id,
percentage=100))
print(net.to_json())
new_file = net.to_json_file("%s.json" % net.id)
################################################################################
### Build Simulation object & save as JSON
sim = Simulation(
id="SimExample11",
network=new_file,
duration="1000",
dt="0.01",
record_traces={"all": "*"},
record_variables={
pynn_receptor_type='inhibitory',
pynn_synapse_type='cond_alpha',
parameters={'e_rev':-80, 'tau_syn':10}))
net.projections.append(Projection(id='proj1',
presynaptic=p0.id,
postsynaptic=p2.id,
synapse='gabaSyn',
delay=2,
weight=0.01))
net.projections[1].random_connectivity=RandomConnectivity(probability=1)'''
net.inputs.append(
Input(id="stim",
input_source=input_source.id,
population=p0.id,
cell_ids=[1, 2]))
print(net.to_json())
new_file = net.to_json_file("%s.json" % net.id)
new_file_yaml = net.to_yaml_file("%s.yaml" % net.id)
################################################################################
### Build Simulation object & save as JSON
sim = Simulation(
id="Sim%s" % net.id,
network=new_file,
duration="100",
dt="0.01",
record_traces={"all": "*"},
def generate(ref, np2=0, np5=0, nb2=0, nb5=0, recordTraces='*'):
################################################################################
### Build new network
net = Network(id=ref)
net.notes = 'Example: %s...' % ref
net.seed = 7890
net.temperature = 32
net.parameters = {
'np2': np2,
'np5': np5,
'nb2': nb2,
'nb5': nb5,
'offset_curr_l2p': -0.05,
'weight_bkg_l2p': 0.01,
'weight_bkg_l5p': 0.01
}
l2p_cell = Cell(id='CELL_HH_reduced_L2Pyr',
neuroml2_source_file='../CELL_HH_reduced_L2Pyr.cell.nml')
net.cells.append(l2p_cell)
l5p_cell = Cell(id='CELL_HH_reduced_L5Pyr',
neuroml2_source_file='../CELL_HH_reduced_L5Pyr.cell.nml')
net.cells.append(l5p_cell)
l2b_cell = Cell(id='CELL_HH_simple_L2Basket',
neuroml2_source_file='../CELL_HH_simple_L2Basket.cell.nml')
net.cells.append(l2b_cell)
l5b_cell = Cell(id='CELL_HH_simple_L5Basket',
neuroml2_source_file='../CELL_HH_simple_L5Basket.cell.nml')
net.cells.append(l5b_cell)
input_source_poisson100 = InputSource(id='poissonFiringSyn100Hz',
neuroml2_source_file='../inputs.nml')
net.input_sources.append(input_source_poisson100)
input_offset_curr_l2p = InputSource(id='input_offset_curr_l2p',
pynn_input='DCSource',
parameters={
'amplitude': 'offset_curr_l2p',
'start': 0,
'stop': 1e9
})
net.input_sources.append(input_offset_curr_l2p)
l2 = RectangularRegion(id='L2',
x=0,
y=1000,
z=0,
width=1000,
height=10,
depth=1000)
net.regions.append(l2)
l5 = RectangularRegion(id='L5',
x=0,
y=0,
z=0,
width=1000,
height=10,
depth=1000)
net.regions.append(l5)
#https://github.com/OpenSourceBrain/OpenCortex
import opencortex.utils.color as occ
pop_l2p = Population(id='pop_l2p',
size='np2',
component=l2p_cell.id,
properties={'color': occ.L23_PRINCIPAL_CELL},
random_layout=RandomLayout(region=l2.id))
net.populations.append(pop_l2p)
pop_l5p = Population(id='pop_l5p',
size='np5',
component=l5p_cell.id,
properties={'color': occ.L5_PRINCIPAL_CELL},
random_layout=RandomLayout(region=l5.id))
net.populations.append(pop_l5p)
pop_l2b = Population(id='pop_l2b',
size='nb2',
component=l2b_cell.id,
properties={'color': occ.L23_INTERNEURON},
random_layout=RandomLayout(region=l2.id))
net.populations.append(pop_l2b)
pop_l5b = Population(id='pop_l5b',
size='nb5',
component=l5b_cell.id,
properties={'color': occ.L5_INTERNEURON},
random_layout=RandomLayout(region=l5.id))
net.populations.append(pop_l5b)
# L2 -> L2
_add_projection(pop_l2p,
pop_l2b,
'AMPA',
delay=0,
weight=0.001,
probability=0.8,
net=net)
_add_projection(pop_l2b,
pop_l2p,
'L2Pyr_GABAA',
delay=0,
weight=0.001,
probability=0.8,
net=net)
_add_projection(pop_l2b,
pop_l2p,
'L2Pyr_GABAB',
delay=0,
weight=0.001,
probability=0.8,
net=net)
# L2 -> L5
_add_projection(pop_l2p,
pop_l5p,
'L5Pyr_AMPA',
delay=0,
weight=0.001,
probability=0.8,
net=net)
_add_projection(pop_l2p,
pop_l5b,
'AMPA',
delay=0,
weight=0.001,
probability=0.8,
net=net)
_add_projection(pop_l2b,
pop_l5p,
'L5Pyr_GABAA',
delay=0,
weight=0.001,
probability=0.8,
net=net)
# L5 -> L5
_add_projection(pop_l5p,
pop_l5b,
'AMPA',
delay=0,
weight=0.001,
probability=0.8,
net=net)
_add_projection(pop_l5b,
pop_l5p,
'L5Pyr_GABAA',
delay=0,
weight=0.001,
probability=0.8,
net=net)
_add_projection(pop_l5b,
pop_l5p,
'L5Pyr_GABAB',
delay=0,
weight=0.001,
probability=0.8,
net=net)
net.inputs.append(
Input(id='stim_%s' % pop_l2p.id,
input_source=input_source_poisson100.id,
population=pop_l2p.id,
percentage=100,
weight='weight_bkg_l2p'))
net.inputs.append(
Input(id='stim_%s' % pop_l5p.id,
input_source=input_source_poisson100.id,
population=pop_l5p.id,
percentage=100,
weight='weight_bkg_l5p'))
print(net.to_json())
new_file = net.to_json_file('%s.json' % net.id)
################################################################################
### Build Simulation object & save as JSON
sim = Simulation(id='Sim%s' % net.id,
network=new_file,
duration='500',
seed='1111',
dt='0.025',
recordTraces={'all': recordTraces},
recordSpikes={'all': '*'})
sim.to_json_file()
print(sim.to_json())
return sim, net
weight = W[pops.index(post)][pops.index(pre)]
print('Connection %s -> %s weight %s'%(pre.id, post.id, weight))
if weight!=0:
net.projections.append(Projection(id='proj_%s_%s'%(pre.id,post.id),
presynaptic=pre.id,
postsynaptic=post.id,
synapse='rs',
type='continuousProjection',
delay=0,
weight=weight,
random_connectivity=RandomConnectivity(probability=1)))
net.inputs.append(Input(id='modulation',
input_source=input_source0.id,
population=pE.id,
percentage=100))
print(net)
print(net.to_json())
new_file = net.to_json_file('%s.json'%net.id)
################################################################################
### Build Simulation object & save as JSON
sim = Simulation(id='Sim%s'%net.id,
network=new_file,
duration='200',
dt='0.025',
recordRates={'all':'*'})
'filename':'test_files/cons_locs_pathways_mc0_Column.h5',
'percentage_cells_per_pop':percent,
'DEFAULT_CELL_ID':default_cell.id,
'cell_info':{default_cell.id:default_cell}})
net.cells.append(default_cell)
net.synapses.append(Synapse(id='ampa', neuroml2_source_file='test_files/ampa.synapse.nml'))
net.synapses.append(Synapse(id='gaba', neuroml2_source_file='test_files/gaba.synapse.nml'))
input_source = InputSource(id='poissonFiringSyn', neuroml2_source_file='test_files/inputs.nml')
net.input_sources.append(input_source)
for pop in ['L4_PC']:
net.inputs.append(Input(id='stim_%s'%pop,
input_source=input_source.id,
population=pop,
percentage=80))
new_file = net.to_json_file('%s.json'%net.id)
################################################################################
### Builds a NeuroML 2 representation, save as XML
format_='xml'
generate_neuroml2_from_network(net,
nml_file_name='%s.net.nml%s'%(net.id, '.h5' if format_=='hdf5' else ''),
format=format_)
from neuromllite import Simulation
def generate():
################################################################################
### Build new network
net = Network(id='ExampleK')
net.notes = 'Example...'
net.parameters = {'pop_size': '8', 'stim_amp': '0.3'}
cell = Cell(id='kuramoto1', lems_source_file='CellExamples.xml')
net.cells.append(cell)
input_source = InputSource(id='iclamp0',
neuroml2_input='PulseGeneratorDL',
parameters={
'amplitude': 'stim_amp',
'delay': '100ms',
'duration': '800ms'
})
net.input_sources.append(input_source)
'''
input_source = InputSource(id='poissonFiringSyn',
neuroml2_input='poissonFiringSynapse',
parameters={'average_rate':"eta", 'synapse':"ampa", 'spike_target':"./ampa"})
'''
r1 = RectangularRegion(id='region1',
x=0,
y=0,
z=0,
width=1000,
height=100,
depth=1000)
net.regions.append(r1)
pE = Population(id='Epop',
size='pop_size',
component=cell.id,
properties={'color': '1 0 0'},
random_layout=RandomLayout(region=r1.id))
net.populations.append(pE)
'''
net.synapses.append(Synapse(id='ampa',
pynn_receptor_type='excitatory',
pynn_synapse_type='curr_alpha',
parameters={'tau_syn':0.1}))
net.projections.append(Projection(id='projEinput',
presynaptic=pEpoisson.id,
postsynaptic=pE.id,
synapse='ampa',
delay=2,
weight=0.02,
one_to_one_connector=OneToOneConnector()))
net.projections.append(Projection(id='projEE',
presynaptic=pE.id,
postsynaptic=pE.id,
synapse='ampa',
delay=2,
weight=0.002,
random_connectivity=RandomConnectivity(probability=.5)))
net.projections.append(Projection(id='projEI',
presynaptic=pE.id,
postsynaptic=pI.id,
synapse='ampa',
delay=2,
weight=0.02,
random_connectivity=RandomConnectivity(probability=.5)))
net.projections.append(Projection(id='projIE',
presynaptic=pI.id,
postsynaptic=pE.id,
synapse='gaba',
delay=2,
weight=0.02,
random_connectivity=RandomConnectivity(probability=.5)))
'''
net.inputs.append(
Input(id='stim',
input_source=input_source.id,
population=pE.id,
percentage=50))
#print(net)
#print(net.to_json())
new_file = net.to_json_file('%s.json' % net.id)
################################################################################
### Build Simulation object & save as JSON
sim = Simulation(id='SimExampleK',
network=new_file,
duration='1000',
dt='0.025',
seed=123,
recordVariables={'sin_theta': {
pE.id: '*'
}})
sim.to_json_file()
return sim, net
syn = Synapse(id='AMPA_preLTP', neuroml2_source_file='fourPathwaySyn.synapse.nml')
syn = Synapse(id='AMPA_postLTD', neuroml2_source_file='fourPathwaySyn.synapse.nml')
net.synapses.append(syn)
net.projections.append(Projection(id='proj0',
presynaptic=p0.id,
postsynaptic=p1.id,
synapse=syn.id,
delay=0,
weight='weight'))
net.projections[0].random_connectivity=RandomConnectivity(probability=1)
net.inputs.append(Input(id='i_stim1',
input_source=stim1.id,
population=p1.id,
percentage=100))
net.inputs.append(Input(id='i_stim2',
input_source=stim2.id,
population=p1.id,
percentage=100))
print(net.to_json())
new_file = net.to_json_file('%s.json'%net.id)
################################################################################
### Build Simulation object & save as JSON
recordVariables={}
syn_vars = ['g', 'u_bar', 'T', 'N_alpha_bar', 'N_beta_bar', 'N_alpha', 'N_beta', 'N', 'Z', 'X', 'w_pre', 'w_post','w', 'G','C','P']
net.synapses.append(Synapse(id='ampa', neuroml2_source_file='test_files/ampa.synapse.nml'))
#net.synapses.append(Synapse(id='gaba', neuroml2_source_file='test_files/gaba.synapse.nml'))
net.projections.append(Projection(id='projEI',
presynaptic=pE.id,
postsynaptic=pRS.id,
synapse='ampa',
delay=2,
weight=0.2,
random_connectivity=RandomConnectivity(probability=.8)))
'''
net.inputs.append(Input(id='stim0',
input_source=input_source_p0.id,
population=pop0.id,
percentage=50,
weight='weightInput'))
net.inputs.append(Input(id='stim1',
input_source=input_source1.id,
population=pop1.id,
percentage=100))
net.inputs.append(Input(id='stim2',
input_source=input_source1.id,
population=pop2.id,
percentage=50))
print(net)
print(net.to_json())
net.populations[0].component = "hhcell"
net.populations[1].component = "hhcell"
net.cells.append(
Cell(id="hhcell", neuroml2_source_file="test_files/hhcell.cell.nml"))
net.synapses.append(
Synapse(id="ampa", neuroml2_source_file="test_files/ampa.synapse.nml"))
input_source = InputSource(id="poissonFiringSyn",
neuroml2_source_file="test_files/inputs.nml")
net.input_sources.append(input_source)
net.inputs.append(
Input(
id="stim_%s" % net.populations[0].id,
input_source=input_source.id,
population=net.populations[0].id,
percentage=80,
))
print(net.to_json())
new_file = net.to_json_file("%s.json" % net.id)
################################################################################
### Use a handler which just prints info on positions, etc.
def_handler = DefaultNetworkHandler()
generate_network(net, def_handler)
################################################################################
### Export to some formats, e.g. try:
weight=weight))
'''
bkgE = InputSource(id='bkgEstim',
pynn_input='DCSource',
parameters={'amplitude':2, 'start':0., 'stop':1e6})
net.input_sources.append(bkgE)
net.inputs.append(Input(id='bkgE',
input_source=bkgE.id,
population=pE.id,
percentage=100))'''
net.inputs.append(
Input(id='modulation',
input_source=input_source0.id,
population=pVIP.id,
percentage=100))
print(net)
print(net.to_json())
new_file = net.to_json_file('%s.json' % net.id)
################################################################################
### Build Simulation object & save as JSON
sim = Simulation(id='SimdelMolinoEtAl',
network=new_file,
duration='200',
dt='0.025',
recordTraces={'all': '*'})
net.populations.append(exc_pop)
net.populations.append(inh_pop)
input_source = InputSource(id='pulseGenerator0',
neuroml2_input='PulseGenerator',
parameters={
'amplitude': '0nA',
'delay': '100.0ms',
'duration': '800.0ms'
})
net.input_sources.append(input_source)
net.inputs.append(
Input(id='stim',
input_source=input_source.id,
population=exc_pop.id,
percentage=100))
exc_syn = Synapse(id='rsExc', lems_source_file='Demirtas_Parameters.xml')
inh_syn = Synapse(id='rsInh', lems_source_file='Demirtas_Parameters.xml')
net.synapses.append(exc_syn)
net.synapses.append(inh_syn)
syns = {exc_pop.id: exc_syn.id, inh_pop.id: inh_syn.id}
W = [['wee', 'wie'], ['wei', 'wii']]
# Add internal connections
pops = [exc_pop, inh_pop]
internal_connections(pops, W)
# Save to JSON format
delay=1,
weight='Wei',
random_connectivity=RandomConnectivity(probability=.8)))
net.projections.append(Projection(id='projIE',
presynaptic=pI.id,
postsynaptic=pE.id,
synapse='gabaSyn',
delay=1,
weight='Wie',
random_connectivity=RandomConnectivity(probability=.8)))
net.inputs.append(Input(id='stim',
input_source=input_source.id,
population=pE.id,
percentage=100,
weight='weightInput'))
print(net)
print(net.to_json())
new_file = net.to_json_file('%s.json'%net.id)
################################################################################
### Build Simulation object & save as JSON
sim = Simulation(id='Sim%s'%net.id,
network=new_file,
duration='1000',
seed='1111',
id="projEI",
presynaptic=pE.id,
postsynaptic=pRS.id,
synapse="ampa",
delay=2,
weight=0.2,
random_connectivity=RandomConnectivity(probability=0.8),
)
)
net.inputs.append(
Input(
id="stim",
input_source=input_source.id,
population=pE.id,
percentage=50,
weight="weightInput",
)
)
print(net)
print(net.to_json())
new_file = net.to_json_file("%s.json" % net.id)
################################################################################
### Build Simulation object & save as JSON
sim = Simulation(
id="SimExample8",
net.parameters['input_delay'] = '20ms'
net.parameters['input_duration'] = '60ms'
#net.parameters['scaling'] = '1nA'
input_source_e = InputSource(id='Exc_in',
neuroml2_input='PulseGenerator',
parameters={
'amplitude': 'exc_input',
'delay': 'input_delay',
'duration': 'input_duration'
})
net.input_sources.append(input_source_e)
net.inputs.append(
Input(id='Exc_stim',
input_source=input_source_e.id,
population=exc_pop.id,
percentage=100))
input_source_i = InputSource(id='Inh_in',
neuroml2_input='PulseGenerator',
parameters={
'amplitude': 'inh_input',
'delay': 'input_delay',
'duration': 'input_duration'
})
net.input_sources.append(input_source_i)
net.inputs.append(
Input(id='Inh_stim',
input_source=input_source_i.id,
population=inh_pop.id,
percentage=100))
def generate():
dt = 0.025
simtime = 1000
################################################################################
### Build new network
net = Network(id='McCPNet')
net.notes = 'Example of simplified McCulloch-Pitts based Network'
net.parameters = {'amp': 1.5, 'scale': 3}
cell = Cell(id='mccp0', lems_source_file='McCPTest.xml')
net.cells.append(cell)
silentDL = Synapse(id='silentSyn_proj0', lems_source_file='McCPTest.xml')
net.synapses.append(silentDL)
rsDL = Synapse(id='rsDL', lems_source_file='McCPTest.xml')
net.synapses.append(rsDL)
r1 = RectangularRegion(id='region1',
x=0,
y=0,
z=0,
width=1000,
height=100,
depth=1000)
net.regions.append(r1)
p0 = Population(id='McCPpop0',
size='1*scale',
component=cell.id,
properties={
'color': '.9 0.9 0',
'radius': 5
},
random_layout=RandomLayout(region=r1.id))
net.populations.append(p0)
p1 = Population(id='McCPpop1',
size='1*scale',
component=cell.id,
properties={
'color': '.9 0 0.9',
'radius': 5
},
random_layout=RandomLayout(region=r1.id))
net.populations.append(p1)
net.projections.append(
Projection(id='proj0',
presynaptic=p0.id,
postsynaptic=p1.id,
synapse=rsDL.id,
pre_synapse=silentDL.id,
type='continuousProjection',
weight='random()',
random_connectivity=RandomConnectivity(probability=0.6)))
'''
net.synapses.append(Synapse(id='ampa',
pynn_receptor_type='excitatory',
pynn_synapse_type='curr_alpha',
parameters={'tau_syn':0.1}))
net.projections.append(Projection(id='proj1',
presynaptic=pEpoisson.id,
postsynaptic=pLNP.id,
synapse='ampa',
delay=0,
weight='in_weight',
random_connectivity=RandomConnectivity(probability=0.7)))'''
input_source0 = InputSource(id='sg0', neuroml2_source_file='inputs.nml')
net.input_sources.append(input_source0)
input_source1 = InputSource(id='sg1', neuroml2_source_file='inputs.nml')
net.input_sources.append(input_source1)
for pop in [p0.id]:
net.inputs.append(
Input(id='stim0_%s' % pop,
input_source=input_source0.id,
population=pop,
percentage=60))
net.inputs.append(
Input(id='stim1_%s' % pop,
input_source=input_source1.id,
population=pop,
percentage=60))
#print(net)
#print(net.to_json())
new_file = net.to_json_file('%s.json' % net.id)
################################################################################
### Build Simulation object & save as JSON
sim = Simulation(id='Sim%s' % net.id,
network=new_file,
duration=simtime,
dt=dt,
seed=123,
recordVariables={
'R': {
'all': '*'
},
'ISyn': {
'all': '*'
}
})
sim.to_json_file()
return sim, net
synapse='ampaSyn',
delay=2,
weight=0.02))
net.projections[0].random_connectivity=RandomConnectivity(probability=1)
net.projections.append(Projection(id='proj1',
presynaptic=p0.id,
postsynaptic=p2.id,
synapse='gabaSyn',
delay=2,
weight=0.01))
net.projections[1].random_connectivity=RandomConnectivity(probability=1)'''
net.inputs.append(
Input(id='stim',
input_source=input_source.id,
population=p0.id,
percentage=50))
print(net.to_json())
new_file = net.to_json_file('%s.json' % net.id)
################################################################################
### Build Simulation object & save as JSON
sim = Simulation(id='SimSonataExample',
network=new_file,
duration='1000',
dt='0.01',
recordTraces={'all': '*'},
recordSpikes={'pop0': '*'})
net.cells.append(default_cell)
net.synapses.append(
Synapse(id="ampa", neuroml2_source_file="test_files/ampa.synapse.nml"))
net.synapses.append(
Synapse(id="gaba", neuroml2_source_file="test_files/gaba.synapse.nml"))
input_source = InputSource(id="poissonFiringSyn",
neuroml2_source_file="test_files/inputs.nml")
net.input_sources.append(input_source)
for pop in ["L4_PC"]:
net.inputs.append(
Input(
id="stim_%s" % pop,
input_source=input_source.id,
population=pop,
percentage=80,
))
new_file = net.to_json_file("%s.json" % net.id)
################################################################################
### Builds a NeuroML 2 representation, save as XML
format_ = "xml"
nml_file_name, nml_doc = generate_neuroml2_from_network(
net,
nml_file_name="%s.net.nml%s" %
(net.id, ".h5" if format_ == "hdf5" else ""),
format=format_,
)
input_sourceI = InputSource(id='noisyCurrentSourceI',
lems_source_file='TestNCS.xml',
parameters={
'mean': '0.0nA',
'stdev': str(stdNoiseI) + 'nA',
'noiseDt': '%sms' % dt,
'delay': '0ms',
'duration': '%sms' % duration
})
net.input_sources.append(input_sourceE)
net.input_sources.append(input_sourceI)
net.inputs.append(
Input(id='stimE',
input_source=input_sourceE.id,
population=pE.id,
percentage=100))
net.inputs.append(
Input(id='stimI',
input_source=input_sourceI.id,
population=pI.id,
percentage=100))
####################### Save network in json file #############################
print(net.to_json())
new_file = net.to_json_file('%s.json' % net.id)
################################################################################
### Build Simulation object & save as JSON
net.regions.append(r2)
net.populations[0].random_layout = RandomLayout(region=r1.id)
net.populations[1].random_layout = RandomLayout(region=r2.id)
net.populations[0].component = 'hhcell'
net.populations[1].component = 'hhcell'
net.cells.append(Cell(id='hhcell', neuroml2_source_file='test_files/hhcell.cell.nml'))
net.synapses.append(Synapse(id='ampa', neuroml2_source_file='test_files/ampa.synapse.nml'))
input_source = InputSource(id='poissonFiringSyn', neuroml2_source_file='test_files/inputs.nml')
net.input_sources.append(input_source)
net.inputs.append(Input(id='stim_%s'%net.populations[0].id,
input_source=input_source.id,
population=net.populations[0].id,
percentage=80))
print(net.to_json())
new_file = net.to_json_file('%s.json'%net.id)
################################################################################
### Use a handler which just prints info on positions, etc.
def_handler = DefaultNetworkHandler()
generate_network(net, def_handler)
################################################################################