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
def generate():
dt = 0.025
simtime = 1000
################################################################################
### Build new network
net = Network(id="Example7_Brunel2000")
net.notes = "Example 7: based on network of Brunel 2000"
net.parameters = {
"g": 4,
"eta": 1,
"order": 5,
"epsilon": 0.1,
"J": 0.1,
"delay": 1.5,
"tauMem": 20.0,
"tauSyn": 0.1,
"tauRef": 2.0,
"U0": 0.0,
"theta": 20.0,
}
cell = Cell(id="ifcell", pynn_cell="IF_curr_alpha")
cell.parameters = {
"tau_m": "tauMem",
"tau_refrac": "tauRef",
"v_rest": "U0",
"v_reset": "U0",
"v_thresh": "theta",
"cm": 0.001,
"i_offset": 0,
}
# cell = Cell(id='hhcell', neuroml2_source_file='test_files/hhcell.cell.nml')
net.cells.append(cell)
poisson_input = Cell(id="poisson_input", pynn_cell="SpikeSourcePoisson")
poisson_input.parameters = {
"rate":
"1000 * (eta*theta/(J*4*order*epsilon*tauMem)) * (4*order*epsilon)",
"start": 0,
"duration": 1e9,
}
net.cells.append(poisson_input)
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="4*order",
component=cell.id,
properties={
"color": ".9 0 0",
"radius": 5
},
random_layout=RandomLayout(region=r1.id),
)
pEpoisson = Population(
id="expoisson",
size="4*order",
component=poisson_input.id,
properties={
"color": "0.9 0.7 0.7",
"radius": 3
},
random_layout=RandomLayout(region=r1.id),
)
pI = Population(
id="Ipop",
size="1*order",
component=cell.id,
properties={
"color": "0 0 .9",
"radius": 5
},
random_layout=RandomLayout(region=r1.id),
)
pIpoisson = Population(
id="inpoisson",
size="1*order",
component=poisson_input.id,
properties={
"color": "0.7 0.7 0.9",
"radius": 3
},
random_layout=RandomLayout(region=r1.id),
)
net.populations.append(pE)
net.populations.append(pEpoisson)
net.populations.append(pI)
net.populations.append(pIpoisson)
net.synapses.append(
Synapse(
id="ampa",
pynn_receptor_type="excitatory",
pynn_synapse_type="curr_alpha",
parameters={"tau_syn": 0.1},
))
net.synapses.append(
Synapse(
id="gaba",
pynn_receptor_type="inhibitory",
pynn_synapse_type="curr_alpha",
parameters={"tau_syn": 0.1},
))
delay_ext = dt
downscale = 1
J_eff = "J*%s" % (downscale)
# synaptic weights, scaled for alpha functions, such that
# for constant membrane potential, charge J would be deposited
fudge = 0.00041363506632638 # ensures dV = J at V=0
JE = "((%s)/tauSyn)*%s" % (J_eff, fudge)
JI = "-1*g*%s" % (JE)
net.projections.append(
Projection(
id="projEinput",
presynaptic=pEpoisson.id,
postsynaptic=pE.id,
synapse="ampa",
delay=delay_ext,
weight=JE,
one_to_one_connector=OneToOneConnector(),
))
net.projections.append(
Projection(
id="projIinput",
presynaptic=pIpoisson.id,
postsynaptic=pI.id,
synapse="ampa",
delay=delay_ext,
weight=JE,
one_to_one_connector=OneToOneConnector(),
))
net.projections.append(
Projection(
id="projEE",
presynaptic=pE.id,
postsynaptic=pE.id,
synapse="ampa",
delay="delay",
weight=JE,
random_connectivity=RandomConnectivity(probability="epsilon"),
))
net.projections.append(
Projection(
id="projEI",
presynaptic=pE.id,
postsynaptic=pI.id,
synapse="ampa",
delay="delay",
weight=JE,
random_connectivity=RandomConnectivity(probability="epsilon"),
))
net.projections.append(
Projection(
id="projIE",
presynaptic=pI.id,
postsynaptic=pE.id,
synapse="gaba",
delay="delay",
weight=JI,
random_connectivity=RandomConnectivity(probability="epsilon"),
))
net.projections.append(
Projection(
id="projII",
presynaptic=pI.id,
postsynaptic=pI.id,
synapse="gaba",
delay="delay",
weight=JI,
random_connectivity=RandomConnectivity(probability="epsilon"),
))
# 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="SimExample7",
network=new_file,
duration=simtime,
dt=dt,
seed=123,
record_traces={
pE.id: [0, 1],
pI.id: [0, 1]
},
record_spikes={
pE.id: "*",
pI.id: "*",
pEpoisson.id: [0, 1, 2, 3, 4],
pIpoisson.id: [0, 1, 2, 3, 4],
},
)
sim.to_json_file()
return sim, net
net.regions.append(r0)
r1 = RectangularRegion(id="region1",
x=0,
y=200,
z=0,
width=1000,
height=100,
depth=1000)
net.regions.append(r1)
p0 = Population(
id="pop0",
size="scale",
component=cell.id,
properties={"color": "1 0 0"},
random_layout=RandomLayout(region=r0.id),
)
net.populations.append(p0)
p1 = Population(
id="pop1",
size="scale",
component=cell.id,
properties={"color": "0 1 0"},
random_layout=RandomLayout(region=r1.id),
)
net.populations.append(p1)
"""
p1 = Population(id='pop1', size=2, component=cell2.id, properties={'color':'0 1 0'},random_layout = RandomLayout(region=r1.id))
p2 = Population(id='pop2', size=1, component=cell2.id, properties={'color':'0 0 1'},random_layout = RandomLayout(region=r1.id))
net.populations.append(p1)
def generate():
dt = 0.025
simtime = 500
################################################################################
### Build new network
net = Network(id='SpikingEI')
net.notes = 'SpikingEI'
net.parameters = {
'order': 5,
'wee': 8,
'wei': 12,
'wie': -12,
'wii': -3,
'w_scale': 0.001,
'in_rate': 400,
'epsilon': 0.5,
'tauMem': 20.0,
'tauSyn': 0.1,
'tauRef': 2.0
}
cell = Cell(id='ifcell', pynn_cell='IF_curr_alpha')
cell.parameters = {
'tau_m': 'tauMem',
'tau_refrac': 'tauRef',
'v_rest': -70,
'v_reset': -70,
'v_thresh': -50,
'cm': 0.001,
"i_offset": 0
}
#cell = Cell(id='hhcell', neuroml2_source_file='test_files/hhcell.cell.nml')
net.cells.append(cell)
poisson_input = Cell(id='poisson_input', pynn_cell='SpikeSourcePoisson')
poisson_input.parameters = {'rate': 'in_rate', 'start': 0, 'duration': 1e9}
net.cells.append(poisson_input)
r1 = RectangularRegion(id='region1',
x=0,
y=0,
z=0,
width=1000,
height=100,
depth=1000)
net.regions.append(r1)
pE = Population(id='Excitatory',
size='4*order',
component=cell.id,
properties={
'color': '.9 0 0',
'radius': 5
},
random_layout=RandomLayout(region=r1.id))
pEpoisson = Population(id='expoisson',
size='4*order',
component=poisson_input.id,
properties={
'color': '0.9 0.7 0.7',
'radius': 3
},
random_layout=RandomLayout(region=r1.id))
pI = Population(id='Inhibitory',
size='1*order',
component=cell.id,
properties={
'color': '0 0 .9',
'radius': 5
},
random_layout=RandomLayout(region=r1.id))
pIpoisson = Population(id='inpoisson',
size='1*order',
component=poisson_input.id,
properties={
'color': '0.7 0.7 0.9',
'radius': 3
},
random_layout=RandomLayout(region=r1.id))
net.populations.append(pE)
net.populations.append(pEpoisson)
net.populations.append(pI)
net.populations.append(pIpoisson)
net.synapses.append(
Synapse(id='ampa',
pynn_receptor_type='excitatory',
pynn_synapse_type='curr_alpha',
parameters={'tau_syn': 0.1}))
net.synapses.append(
Synapse(id='gaba',
pynn_receptor_type='inhibitory',
pynn_synapse_type='curr_alpha',
parameters={'tau_syn': 0.1}))
delay_ext = dt
net.projections.append(
Projection(id='projEinput',
presynaptic=pEpoisson.id,
postsynaptic=pE.id,
synapse='ampa',
delay=delay_ext,
weight=0.01,
one_to_one_connector=OneToOneConnector()))
net.projections.append(
Projection(id='projIinput',
presynaptic=pIpoisson.id,
postsynaptic=pI.id,
synapse='ampa',
delay=delay_ext,
weight=0.01,
one_to_one_connector=OneToOneConnector()))
net.projections.append(
Projection(
id='projEE',
presynaptic=pE.id,
postsynaptic=pE.id,
synapse='ampa',
delay=delay_ext,
weight='wee * w_scale',
random_connectivity=RandomConnectivity(probability='epsilon')))
net.projections.append(
Projection(
id='projEI',
presynaptic=pE.id,
postsynaptic=pI.id,
synapse='ampa',
delay=delay_ext,
weight='wei * w_scale',
random_connectivity=RandomConnectivity(probability='epsilon')))
net.projections.append(
Projection(
id='projIE',
presynaptic=pI.id,
postsynaptic=pE.id,
synapse='gaba',
delay=delay_ext,
weight='wie * w_scale',
random_connectivity=RandomConnectivity(probability='epsilon')))
net.projections.append(
Projection(
id='projII',
presynaptic=pI.id,
postsynaptic=pI.id,
synapse='gaba',
delay=delay_ext,
weight='wii * w_scale',
random_connectivity=RandomConnectivity(probability='epsilon')))
#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='SimSpiking',
network=new_file,
duration=simtime,
dt=dt,
seed=123,
recordTraces={
pE.id: '*',
pI.id: '*'
},
recordSpikes={'all': '*'})
sim.to_json_file()
return sim, net
def generate():
dt = 0.025
simtime = 1000
################################################################################
### Build new network
net = Network(id='Example7_Brunel2000')
net.notes = 'Example 7: based on network of Brunel 2000'
net.parameters = { 'g': 4,
'eta': 1,
'order': 5,
'epsilon': 0.1,
'J': 0.1,
'delay': 1.5,
'tauMem': 20.0,
'tauSyn': 0.1,
'tauRef': 2.0,
'U0': 0.0,
'theta': 20.0}
cell = Cell(id='ifcell', pynn_cell='IF_curr_alpha')
cell.parameters = { 'tau_m': 'tauMem',
'tau_refrac': 'tauRef',
'v_rest': 'U0',
'v_reset': 'U0',
'v_thresh': 'theta',
'cm': 0.001,
"i_offset": 0}
#cell = Cell(id='hhcell', neuroml2_source_file='test_files/hhcell.cell.nml')
net.cells.append(cell)
poisson_input = Cell(id='poisson_input', pynn_cell='SpikeSourcePoisson')
poisson_input.parameters = { 'rate': '1000 * (eta*theta/(J*4*order*epsilon*tauMem)) * (4*order*epsilon)',
'start': 0,
'duration': 1e9}
net.cells.append(poisson_input)
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='4*order',
component=cell.id,
properties={'color':'.9 0 0', 'radius':5},
random_layout = RandomLayout(region=r1.id))
pEpoisson = Population(id='expoisson',
size='4*order',
component=poisson_input.id,
properties={'color':'0.9 0.7 0.7', 'radius':3},
random_layout = RandomLayout(region=r1.id))
pI = Population(id='Ipop',
size='1*order',
component=cell.id,
properties={'color':'0 0 .9', 'radius':5},
random_layout = RandomLayout(region=r1.id))
pIpoisson = Population(id='inpoisson',
size='1*order',
component=poisson_input.id,
properties={'color':'0.7 0.7 0.9', 'radius':3},
random_layout = RandomLayout(region=r1.id))
net.populations.append(pE)
net.populations.append(pEpoisson)
net.populations.append(pI)
net.populations.append(pIpoisson)
net.synapses.append(Synapse(id='ampa',
pynn_receptor_type='excitatory',
pynn_synapse_type='curr_alpha',
parameters={'tau_syn':0.1}))
net.synapses.append(Synapse(id='gaba',
pynn_receptor_type='inhibitory',
pynn_synapse_type='curr_alpha',
parameters={'tau_syn':0.1}))
delay_ext = dt
downscale = 1
J_eff = 'J*%s'%(downscale)
# synaptic weights, scaled for alpha functions, such that
# for constant membrane potential, charge J would be deposited
fudge = 0.00041363506632638 # ensures dV = J at V=0
JE = '((%s)/tauSyn)*%s'%(J_eff,fudge)
JI = '-1*g*%s'%(JE)
net.projections.append(Projection(id='projEinput',
presynaptic=pEpoisson.id,
postsynaptic=pE.id,
synapse='ampa',
delay=delay_ext,
weight=JE,
one_to_one_connector=OneToOneConnector()))
net.projections.append(Projection(id='projIinput',
presynaptic=pIpoisson.id,
postsynaptic=pI.id,
synapse='ampa',
delay=delay_ext,
weight=JE,
one_to_one_connector=OneToOneConnector()))
net.projections.append(Projection(id='projEE',
presynaptic=pE.id,
postsynaptic=pE.id,
synapse='ampa',
delay='delay',
weight=JE,
random_connectivity=RandomConnectivity(probability='epsilon')))
net.projections.append(Projection(id='projEI',
presynaptic=pE.id,
postsynaptic=pI.id,
synapse='ampa',
delay='delay',
weight=JE,
random_connectivity=RandomConnectivity(probability='epsilon')))
net.projections.append(Projection(id='projIE',
presynaptic=pI.id,
postsynaptic=pE.id,
synapse='gaba',
delay='delay',
weight=JI,
random_connectivity=RandomConnectivity(probability='epsilon')))
net.projections.append(Projection(id='projII',
presynaptic=pI.id,
postsynaptic=pI.id,
synapse='gaba',
delay='delay',
weight=JI,
random_connectivity=RandomConnectivity(probability='epsilon')))
#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='SimExample7',
network=new_file,
duration=simtime,
dt=dt,
seed= 123,
recordTraces={pE.id:[0,1],pI.id:[0,1]},
recordSpikes={pE.id:'*', pI.id:'*',pEpoisson.id:[0,1,2,3,4],pIpoisson.id:[0,1,2,3,4]})
sim.to_json_file()
return sim, net
def generate():
dt = 0.05
simtime = 100
################################################################################
### Build new network
net = Network(id="FN")
net.notes = "FitzHugh Nagumo cell model - originally specified in NeuroML/LEMS"
net.parameters = {
"initial_w": 0.0,
"initial_v": -1,
"a_v": -0.3333333333333333,
"b_v": 0.0,
"c_v": 1.0,
"d_v": 1,
"e_v": -1.0,
"f_v": 1.0,
"time_constant_v": 1.0,
"a_w": 1.0,
"b_w": -0.8,
"c_w": 0.7,
"time_constant_w": 12.5,
"threshold": -1.0,
"mode": 1.0,
"uncorrelated_activity": 0.0,
"Iext": 0,
}
cellInput = Cell(id="fn",
lems_source_file="FN_Definitions.xml",
parameters={})
for p in net.parameters:
cellInput.parameters[p] = p
net.cells.append(cellInput)
r1 = RectangularRegion(id="region1",
x=0,
y=0,
z=0,
width=1000,
height=100,
depth=1000)
net.regions.append(r1)
pop = Population(
id="FNpop",
size="1",
component=cellInput.id,
properties={
"color": "0.2 0.2 0.2",
"radius": 3
},
random_layout=RandomLayout(region=r1.id),
)
net.populations.append(pop)
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={
"V": {
"all": "*"
},
"W": {
"all": "*"
}
},
plots2D={
"VW": {
"x_axis": "%s/0/fn/V" % pop.id,
"y_axis": "%s/0/fn/W" % pop.id
}
},
)
sim.to_json_file()
return sim, net
def generate():
dt = 0.1
simtime = 1
################################################################################
### Build new network
net = Network(id='ABC')
net.notes = 'Example of simplified network'
net.parameters = {'A_initial': 0, 'A_slope': 5}
cellInput = Cell(id='a_input',
lems_source_file='PNL.xml',
parameters={'variable': 'A_initial'})
net.cells.append(cellInput)
cellA = Cell(id='a',
lems_source_file='PNL.xml',
parameters={'slope': 'A_slope'})
net.cells.append(cellA)
cellB = Cell(id='b', lems_source_file='PNL.xml')
net.cells.append(cellB)
cellC = Cell(id='c', lems_source_file='PNL.xml')
net.cells.append(cellC)
rsDL = Synapse(id='rsDL', lems_source_file='PNL.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)
pAin = Population(id='A_input',
size='1',
component=cellInput.id,
properties={
'color': '0.2 0.2 0.2',
'radius': 3
},
random_layout=RandomLayout(region=r1.id))
net.populations.append(pAin)
pA = Population(id='A',
size='1',
component=cellA.id,
properties={
'color': '0 0.9 0',
'radius': 5
},
random_layout=RandomLayout(region=r1.id))
net.populations.append(pA)
pB = Population(id='B',
size='1',
component=cellB.id,
properties={
'color': '.9 0 0',
'radius': 5
},
random_layout=RandomLayout(region=r1.id))
net.populations.append(pB)
pC = Population(id='C',
size='1',
component=cellC.id,
properties={
'color': '0.7 0 0',
'radius': 5
},
random_layout=RandomLayout(region=r1.id))
net.populations.append(pC)
silentDLin = Synapse(id='silentSyn_proj_input', lems_source_file='PNL.xml')
net.synapses.append(silentDLin)
net.projections.append(
Projection(id='proj_input',
presynaptic=pA.id,
postsynaptic=pB.id,
synapse=rsDL.id,
pre_synapse=silentDLin.id,
type='continuousProjection',
weight=1,
random_connectivity=RandomConnectivity(probability=1)))
silentDL0 = Synapse(id='silentSyn_proj0', lems_source_file='PNL.xml')
net.synapses.append(silentDL0)
net.projections.append(
Projection(id='proj0',
presynaptic=pAin.id,
postsynaptic=pA.id,
synapse=rsDL.id,
pre_synapse=silentDL0.id,
type='continuousProjection',
weight=1,
random_connectivity=RandomConnectivity(probability=1)))
silentDL1 = Synapse(id='silentSyn_proj1', lems_source_file='PNL.xml')
net.synapses.append(silentDL1)
net.projections.append(
Projection(id='proj1',
presynaptic=pA.id,
postsynaptic=pC.id,
synapse=rsDL.id,
pre_synapse=silentDL1.id,
type='continuousProjection',
weight=1,
random_connectivity=RandomConnectivity(probability=1)))
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={
'OUTPUT': {
'all': '*'
},
'INPUT': {
'all': '*'
}
})
sim.to_json_file()
return sim, net
pI = Population(id='Ipop',
size='N - int(N*fraction_E)',
component=cell.id,
properties={'color': '.8 0 0'})
bkgPre = Population(id='Background_pre',
size='int(N*fraction_E)',
component=ssp_pre.id,
properties={'color': '.8 .8 .8'})
net.populations.append(pE)
net.populations.append(pI)
net.populations.append(bkgPre)
for p in net.populations:
p.random_layout = RandomLayout(region=r.id)
net.synapses.append(
Synapse(id='ampa',
pynn_receptor_type='excitatory',
pynn_synapse_type='cond_alpha',
parameters={
'e_rev': nesp['E_ex'],
'tau_syn': nesp['tau_syn_ex']
}))
net.synapses.append(
Synapse(id='gaba',
pynn_receptor_type='inhibitory',
pynn_synapse_type='cond_alpha',
parameters={
def generate():
dt = 0.025
simtime = 1000
################################################################################
### Build new network
net = Network(id='ExampleIF')
net.notes = 'Example with IF'
net.parameters = {
'tauMem': 20.0,
'tauSyn': 0.1,
'tauRef': 2,
'V0': -70,
'theta': -50.0,
'scale': 1,
'in_weight': 0.01,
'in_rate': 50
}
ifcell = Cell(id='ifcell', pynn_cell='IF_curr_alpha')
ifcell.parameters = {
'tau_m': 'tauMem',
'tau_refrac': 'tauRef',
'v_rest': 'V0',
'v_reset': 'V0',
'v_thresh': 'theta',
'cm': 0.001,
"i_offset": 0
}
net.cells.append(ifcell)
poisson_input = Cell(id='poisson_input', pynn_cell='SpikeSourcePoisson')
poisson_input.parameters = {'rate': 'in_rate', 'start': 0, 'duration': 1e9}
net.cells.append(poisson_input)
r1 = RectangularRegion(id='region1',
x=0,
y=0,
z=0,
width=1000,
height=100,
depth=1000)
net.regions.append(r1)
pIF = Population(id='IFpop',
size='1*scale',
component=ifcell.id,
properties={
'color': '.9 0 0',
'radius': 5
},
random_layout=RandomLayout(region=r1.id))
net.populations.append(pIF)
pLNP = Population(id='LNPpop',
size='1*scale',
component=ifcell.id,
properties={
'color': '.9 0.9 0',
'radius': 5
},
random_layout=RandomLayout(region=r1.id))
net.populations.append(pLNP)
pEpoisson = Population(id='expoisson',
size='10',
component=poisson_input.id,
properties={
'color': '0.9 0.7 0.7',
'radius': 3
},
random_layout=RandomLayout(region=r1.id))
net.populations.append(pEpoisson)
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='proj0',
presynaptic=pEpoisson.id,
postsynaptic=pIF.id,
synapse='ampa',
delay=0,
weight='in_weight',
random_connectivity=RandomConnectivity(probability=0.7)))
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)))
#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='SimExampleIF',
network=new_file,
duration=simtime,
dt=dt,
seed=123,
recordTraces={pIF.id: '*'},
recordSpikes={'all': '*'})
sim.to_json_file()
return sim, net
def generate():
dt = 0.05
simtime = 100
################################################################################
### Build new network
net = Network(id='FN')
net.notes = 'Example of simplified network'
net.parameters = { 'initial_w': 0.0,
'initial_v': -1,
'a_v': -0.3333333333333333,
'b_v': 0.0,
'c_v': 1.0,
'd_v': 1,
'e_v': -1.0,
'f_v': 1.0,
'time_constant_v': 1.0,
'a_w': 1.0,
'b_w': -0.8,
'c_w': 0.7,
'time_constant_w': 12.5,
'threshold': -1.0,
'mode': 1.0,
'uncorrelated_activity': 0.0,
'Iext': 0 }
cellInput = Cell(id='fn',
lems_source_file='FN_Definitions.xml',
parameters={})
for p in net.parameters:
cellInput.parameters[p]=p
net.cells.append(cellInput)
r1 = RectangularRegion(id='region1', x=0,y=0,z=0,width=1000,height=100,depth=1000)
net.regions.append(r1)
pop = Population(id='FNpop',
size='1',
component=cellInput.id,
properties={'color':'0.2 0.2 0.2', 'radius':3},
random_layout = RandomLayout(region=r1.id))
net.populations.append(pop)
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={'V':{'all':'*'},'W':{'all':'*'}},
plots2D={'VW':{'x_axis':'%s/0/fn/V'%pop.id,
'y_axis':'%s/0/fn/W'%pop.id}})
sim.to_json_file()
return sim, net
stim1 = InputSource(id='stim1',
pynn_input='DCSource',
parameters={'amplitude':0.4, 'start':'stim1_delay', 'stop':'stim1_delay+5'})
net.input_sources.append(stim1)
stim2 = InputSource(id='stim2',
pynn_input='DCSource',
parameters={'amplitude':0.4, 'start':'stim2_delay', 'stop':'stim2_delay+5'})
net.input_sources.append(stim2)
r1 = RectangularRegion(id='region1', x=0,y=0,z=0,width=1000,height=100,depth=1000)
net.regions.append(r1)
p0 = Population(id='pop0', size=1, component=spkArr1.id, properties={'color':'1 0 0', 'radius':10},random_layout = RandomLayout(region=r1.id))
p1 = Population(id='pop1', size=1, component=cell.id, properties={'color':'0 1 0'},random_layout = RandomLayout(region=r1.id))
net.populations.append(p0)
net.populations.append(p1)
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,
y=0,
z=0,
width=1000,
height=100,
depth=1000)
r2 = RectangularRegion(id="region2",
x=0,
y=200,
z=0,
width=1000,
height=100,
depth=1000)
net.regions.append(r1)
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(
def generate():
dt = 100 # ms, so 0.1s
simtime = 5000 # ms, so 50s
################################################################################
### Build new network
net = Network(id="ABCD")
net.notes = "Example of a simplified network"
net.parameters = {"A_initial": 0.1, "A_slope": 2.2}
cellInput = Cell(id="a_input",
lems_source_file="PNL.xml",
parameters={"variable": "A_initial"})
net.cells.append(cellInput)
cellA = Cell(id="a", lems_source_file="PNL.xml")
net.cells.append(cellA)
cellB = Cell(id="b", lems_source_file="PNL.xml")
net.cells.append(cellB)
cellC = Cell(id="c", lems_source_file="PNL.xml")
net.cells.append(cellC)
cellD = Cell(id="d", lems_source_file="PNL.xml")
net.cells.append(cellD)
rsDL = Synapse(id="rsDL", lems_source_file="PNL.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)
pAin = Population(
id="A_input",
size="1",
component=cellInput.id,
properties={
"color": "0.2 0.2 0.2",
"radius": 3
},
random_layout=RandomLayout(region=r1.id),
)
net.populations.append(pAin)
pA = Population(
id="A",
size="1",
component=cellA.id,
properties={
"color": "0 0.9 0",
"radius": 5
},
random_layout=RandomLayout(region=r1.id),
)
net.populations.append(pA)
pB = Population(
id="B",
size="1",
component=cellB.id,
properties={
"color": ".8 .8 .8",
"radius": 5
},
random_layout=RandomLayout(region=r1.id),
)
net.populations.append(pB)
pC = Population(
id="C",
size="1",
component=cellC.id,
properties={
"color": "0.7 0.7 0.7",
"radius": 5
},
random_layout=RandomLayout(region=r1.id),
)
net.populations.append(pC)
pD = Population(
id="D",
size="1",
component=cellD.id,
properties={
"color": "0.7 0 0",
"radius": 5
},
random_layout=RandomLayout(region=r1.id),
)
net.populations.append(pD)
silentDLin = Synapse(id="silentSyn_proj_input", lems_source_file="PNL.xml")
net.synapses.append(silentDLin)
net.projections.append(
Projection(
id="proj_input",
presynaptic=pA.id,
postsynaptic=pB.id,
synapse=rsDL.id,
pre_synapse=silentDLin.id,
type="continuousProjection",
weight=1,
random_connectivity=RandomConnectivity(probability=1),
))
silentDL0 = Synapse(id="silentSyn_proj0", lems_source_file="PNL.xml")
net.synapses.append(silentDL0)
net.projections.append(
Projection(
id="proj0",
presynaptic=pAin.id,
postsynaptic=pA.id,
synapse=rsDL.id,
pre_synapse=silentDL0.id,
type="continuousProjection",
weight=1,
random_connectivity=RandomConnectivity(probability=1),
))
silentDL1 = Synapse(id="silentSyn_proj1", lems_source_file="PNL.xml")
net.synapses.append(silentDL1)
net.projections.append(
Projection(
id="proj1",
presynaptic=pA.id,
postsynaptic=pC.id,
synapse=rsDL.id,
pre_synapse=silentDL1.id,
type="continuousProjection",
weight=1,
random_connectivity=RandomConnectivity(probability=1),
))
silentDL2 = Synapse(id="silentSyn_proj2", lems_source_file="PNL.xml")
net.synapses.append(silentDL2)
net.projections.append(
Projection(
id="proj2",
presynaptic=pB.id,
postsynaptic=pD.id,
synapse=rsDL.id,
pre_synapse=silentDL2.id,
type="continuousProjection",
weight=1,
random_connectivity=RandomConnectivity(probability=1),
))
silentDL3 = Synapse(id="silentSyn_proj3", lems_source_file="PNL.xml")
net.synapses.append(silentDL3)
net.projections.append(
Projection(
id="proj3",
presynaptic=pC.id,
postsynaptic=pD.id,
synapse=rsDL.id,
pre_synapse=silentDL3.id,
type="continuousProjection",
weight=1,
random_connectivity=RandomConnectivity(probability=1),
))
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={"OUTPUT": {
"all": "*"
}},
) # ,'INPUT':{'all':'*'}
sim.to_json_file()
return sim, net
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
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
######################### Spatial parameters for network ######################
r1 = RectangularRegion(id='region1',
x=0,
y=0,
z=0,
width=1000,
height=100,
depth=1000)
net.regions.append(r1)
############################# Populations #####################################
pE = Population(id='popE',
size='int(1600*scale)',
component=cellE.id,
properties={'color': '.9 0 0'},
random_layout=RandomLayout(region=r1.id))
pI = Population(id='popI',
size='int(400*scale)',
component=cellI.id,
properties={'color': '0 0 .9'},
random_layout=RandomLayout(region=r1.id))
### Append populations to network
net.populations.append(pE)
net.populations.append(pI)
############################### Inputs (Noise) ################################
# Noise on excitatory neurons
stdNoiseE = (sigmaV / R) * (tauE_m**0.5) / (dt**0.5)
input_sourceE = InputSource(id='noisyCurrentSourceE',
net.input_sources.append(input_source0)
r1 = RectangularRegion(id='network', x=0,y=0,z=0,width=100,height=100,depth=10)
net.regions.append(r1)
colors = [[8,48,107], # dark-blue
[228,26,28]]
color_str = {}
for i in range(len(colors)):
color_str[i] = ''
for c in colors[i]:
color_str[i]+='%s '%(c/255.)
color_str[i] = color_str[i][:-1]
pE = Population(id='Exc', size=1, component=ecell.id, properties={'color':color_str[0]},random_layout = RandomLayout(region=r1.id))
pI = Population(id='Inh', size=1, component=icell.id, properties={'color':color_str[1]},random_layout = RandomLayout(region=r1.id))
net.populations.append(pE)
net.populations.append(pI)
pops = [pE,pI]
net.synapses.append(Synapse(id='rs',
lems_source_file='SimpleExamples.xml'))
W = [[2.4167, -0.3329],
[2.9706, -3.4554]]
W = [[0, 0],