'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))
# Save to JSON format
net.id = 'WC'
new_file = net.to_json_file('WC.json')
sim = Simulation(id='SimWC',
duration='100',
dt='0.005',
network=new_file,
recordRates={'all': '*'},
plots2D={
'E-I': {
'x_axis': 'Excitatory/0/Exc/r',
'y_axis': 'Inhibitory/0/Inh/r'
}
})
sim.to_json_file('SimWC.nmllite.json')
check_to_generate_or_run(sys.argv, sim)
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 = 0.025
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='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
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(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
duration='1000',
dt='0.1',
network=new_file,
recordVariables={
'r': {
'all': '*'
},
'e': {
'all': '*'
},
'f': {
'all': '*'
},
'iTotal': {
'all': '*'
},
'nu': {
'all': '*'
},
'internalNoise': {
'all': '*'
},
'S': {
'all': '*'
}
})
sim.to_json_file('SimDemirtas_network.nmllite.json')
check_to_generate_or_run(sys.argv, sim)
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
def generate():
################################################################################
### Build new network
net = Network(id='RunStims')
net.notes = 'Example with spike producers'
net.parameters = { 'rate': 50,
'rateHz': '50Hz',
'periodms': '20ms'}
ssp = Cell(id='ssp', pynn_cell='SpikeSourcePoisson')
ssp.parameters = { 'rate': 'rate',
'start': 0,
'duration': 1e9}
net.cells.append(ssp)
sspPop = Population(id='sspPop', size=1, component=ssp.id, properties={'color':'.5 0 0'})
net.populations.append(sspPop)
sg = Cell(id='sg', neuroml2_cell='SpikeGenerator')
sg.parameters = { 'period': 'periodms'}
net.cells.append(sg)
sgPop = Population(id='sgPop', size=1, component=sg.id, properties={'color':'.5 0 0'})
net.populations.append(sgPop)
sgp = Cell(id='sgp', neuroml2_cell='spikeGeneratorPoisson')
sgp.parameters = { 'average_rate': 'rateHz'}
net.cells.append(sgp)
sgpPop = Population(id='sgpPop', size=1, component=sgp.id, properties={'color':'.5 0 0'})
net.populations.append(sgpPop)
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}))
#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='SimTest',
network=new_file,
duration='10000',
dt='0.025',
seed= 123,
recordTraces={'xxx':'*'},
recordSpikes={'all':'*'})
sim.to_json_file()
return sim, net
def generate(ref='Example6_PyNN', add_inputs=True):
################################################################################
### Build new network
net = Network(id=ref,
notes='Another network for PyNN - work in progress...')
net.parameters = {
'N_scaling': 0.005,
'layer_height': 400,
'width': 100,
'depth': 100,
'input_weight': 0.1
}
cell = Cell(id='CorticalCell', pynn_cell='IF_curr_exp')
cell.parameters = {
'cm': 0.25, # nF
'i_offset': 0.0, # nA
'tau_m': 10.0, # ms
'tau_refrac': 2.0, # ms
'v_reset': -65.0, # mV
'v_rest': -65.0, # mV
'v_thresh': -50.0 # mV
}
net.cells.append(cell)
if add_inputs:
input_cell = Cell(id='InputCell', pynn_cell='SpikeSourcePoisson')
input_cell.parameters = {
'start': 0,
'duration': 10000000000,
'rate': 150
}
net.cells.append(input_cell)
e_syn = Synapse(id='ampa',
pynn_receptor_type='excitatory',
pynn_synapse_type='curr_exp',
parameters={'tau_syn': 0.5})
net.synapses.append(e_syn)
i_syn = Synapse(id='gaba',
pynn_receptor_type='inhibitory',
pynn_synapse_type='curr_exp',
parameters={'tau_syn': 0.5})
net.synapses.append(i_syn)
N_full = {
'L23': {
'E': 20683,
'I': 5834
},
'L4': {
'E': 21915,
'I': 5479
},
'L5': {
'E': 4850,
'I': 1065
},
'L6': {
'E': 14395,
'I': 2948
}
}
scale = 0.1
pops = []
input_pops = []
pop_dict = {}
layers = ['L23']
layers = ['L23', 'L4', 'L5', 'L6']
for l in layers:
i = 3 - layers.index(l)
r = RectangularRegion(id=l,
x=0,
y=i * net.parameters['layer_height'],
z=0,
width=net.parameters['width'],
height=net.parameters['layer_height'],
depth=net.parameters['depth'])
net.regions.append(r)
for t in ['E', 'I']:
try:
import opencortex.utils.color as occ
if l == 'L23':
if t == 'E': color = occ.L23_PRINCIPAL_CELL
if t == 'I': color = occ.L23_INTERNEURON
if l == 'L4':
if t == 'E': color = occ.L4_PRINCIPAL_CELL
if t == 'I': color = occ.L4_INTERNEURON
if l == 'L5':
if t == 'E': color = occ.L5_PRINCIPAL_CELL
if t == 'I': color = occ.L5_INTERNEURON
if l == 'L6':
if t == 'E': color = occ.L6_PRINCIPAL_CELL
if t == 'I': color = occ.L6_INTERNEURON
except:
color = '.8 0 0' if t == 'E' else '0 0 1'
pop_id = '%s_%s' % (l, t)
pops.append(pop_id)
ref = 'l%s%s' % (l[1:], t.lower())
exec(
ref +
" = Population(id=pop_id, size='int(%s*N_scaling)'%N_full[l][t], component=cell.id, properties={'color':color, 'type':t})"
)
exec("%s.random_layout = RandomLayout(region = r.id)" % ref)
exec("net.populations.append(%s)" % ref)
exec("pop_dict['%s'] = %s" % (pop_id, ref))
if add_inputs:
color = '.8 .8 .8'
input_id = '%s_%s_input' % (l, t)
input_pops.append(input_id)
input_ref = 'l%s%s_i' % (l[1:], t.lower())
exec(
input_ref +
" = Population(id=input_id, size='int(%s*N_scaling)'%N_full[l][t], component=input_cell.id, properties={'color':color})"
)
exec("%s.random_layout = RandomLayout(region = r.id)" %
input_ref)
exec("net.populations.append(%s)" % input_ref)
#l23i = Population(id='L23_I', size=int(100*scale), component=cell.id, properties={'color':})
#l23ei = Population(id='L23_E_input', size=int(100*scale), component=input_cell.id)
#l23ii = Population(id='L23_I_input', size=int(100*scale), component=input_cell.id)
#net.populations.append(l23e)
#net.populations.append(l23ei)
#net.populations.append(l23i)
#net.populations.append(l23ii)
conn_probs = [
[0.1009, 0.1689, 0.0437, 0.0818, 0.0323, 0., 0.0076, 0.],
[0.1346, 0.1371, 0.0316, 0.0515, 0.0755, 0., 0.0042, 0.],
[0.0077, 0.0059, 0.0497, 0.135, 0.0067, 0.0003, 0.0453, 0.],
[0.0691, 0.0029, 0.0794, 0.1597, 0.0033, 0., 0.1057, 0.],
[0.1004, 0.0622, 0.0505, 0.0057, 0.0831, 0.3726, 0.0204, 0.],
[0.0548, 0.0269, 0.0257, 0.0022, 0.06, 0.3158, 0.0086, 0.],
[0.0156, 0.0066, 0.0211, 0.0166, 0.0572, 0.0197, 0.0396, 0.2252],
[0.0364, 0.001, 0.0034, 0.0005, 0.0277, 0.008, 0.0658, 0.1443]
]
if add_inputs:
for p in pops:
proj = Projection(id='proj_input_%s' % p,
presynaptic='%s_input' % p,
postsynaptic=p,
synapse=e_syn.id,
delay=2,
weight='input_weight')
proj.one_to_one_connector = OneToOneConnector()
net.projections.append(proj)
for pre_i in range(len(pops)):
for post_i in range(len(pops)):
pre = pops[pre_i]
post = pops[post_i]
prob = conn_probs[post_i][pre_i] ####### TODO: check!!!!
weight = 1
syn = e_syn
if prob > 0:
if 'I' in pre:
weight = -1
syn = i_syn
proj = Projection(id='proj_%s_%s' % (pre, post),
presynaptic=pre,
postsynaptic=post,
synapse=syn.id,
delay=1,
weight=weight)
proj.random_connectivity = RandomConnectivity(probability=prob)
net.projections.append(proj)
print(net.to_json())
new_file = net.to_json_file('%s.json' % net.id)
################################################################################
### Build Simulation object & save as JSON
recordTraces = {}
recordSpikes = {}
from neuromllite.utils import evaluate
for p in pops:
forecast_size = evaluate(pop_dict[p].size, net.parameters)
recordTraces[p] = list(range(min(2, forecast_size)))
recordSpikes[p] = '*'
for ip in input_pops:
recordSpikes[ip] = '*'
sim = Simulation(id='Sim%s' % net.id,
network=new_file,
duration='100',
dt='0.025',
seed=1234,
recordTraces=recordTraces,
recordSpikes=recordSpikes)
sim.to_json_file()
return sim, net
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=pc_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=go_pop.id,
percentage=100))'''
# Save to JSON format
new_file = net.to_json_file('%s.json' % net.id)
sim = Simulation(id='Sim%s' % net.id,
duration='100',
dt='0.005',
network=new_file,
recordRates={'all': '*'})
sim.to_json_file('Sim%s.nmllite.json' % net.id)
check_to_generate_or_run(sys.argv, sim)
pops = [exc_pop, inh_pop]
internal_connections(pops, W, syns)
# Save to JSON format
net.id = 'Joglekar_figure1b'
new_file = net.to_json_file('Joglekar_figure1c.json')
sim = Simulation(id='SimJoglekar_figure1b',
duration='2',
dt='0.02',
network=new_file,
recordVariables={'r': {
'all': '*'
}})
sim.to_json_file('SimJoglekar_figure1c.nmllite.json')
check_to_generate_or_run(sys.argv, sim)
# Open data file
data = np.loadtxt('Excitatory_' + str(round(wEE, 5)) + '_' +
str(round(wEI, 5)) + '_0.r.dat')
maxamplocal[j, i] = np.max(data[:, 1])
for file in glob.glob('./*.{}'.format('dat')):
print(file)
shutil.move(file, './Files/')
### Abrir arquivo usando pandas e salvar o maximo em uma matriz se o autovalor for positivo
else:
# set value -5 to unstable region (it helps to plot the colormap)
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():
################################################################################
### 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 = 0.1
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
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
def generate():
################################################################################
### 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)
expoisson = Cell(id='expoisson', pynn_cell='SpikeSourcePoisson')
expoisson.parameters = {
'rate':
'1000 * (eta*theta/(J*4*order*epsilon*tauMem)) * (4*order*epsilon)',
'start': 0,
'duration': 1e9
}
net.cells.append(expoisson)
'''
input_source = InputSource(id='iclamp0',
pynn_input='DCSource',
parameters={'amplitude':0.002, 'start':100., 'stop':900.})
input_source = InputSource(id='poissonFiringSyn',
neuroml2_input='poissonFiringSynapse',
parameters={'average_rate':"eta", 'synapse':"ampa", 'spike_target':"./ampa"})
net.input_sources.append(input_source)'''
pE = Population(id='Epop',
size='4*order',
component=cell.id,
properties={'color': '1 0 0'})
pEpoisson = Population(id='Einput',
size='4*order',
component=expoisson.id,
properties={'color': '.5 0 0'})
pI = Population(id='Ipop',
size='1*order',
component=cell.id,
properties={'color': '0 0 1'})
net.populations.append(pE)
net.populations.append(pEpoisson)
net.populations.append(pI)
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}))
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='SimExample7',
network=new_file,
duration='1000',
dt='0.025',
seed=123,
recordTraces={
pE.id: '*',
pI.id: '*'
},
recordSpikes={'all': '*'})
sim.to_json_file()
return sim, net
####################### 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
sim = Simulation(id='SimJoglekar1Network',
network=new_file,
duration=duration,
dt=dt,
seed=1234,
recordTraces={
pE.id: [0, 1],
pI.id: [0, 1]
},
recordSpikes={
pE.id: '*',
pI.id: '*'
})
sim.to_json_file()
################################################################################
### Run in some simulators
from neuromllite.NetworkGenerator import check_to_generate_or_run
import sys
check_to_generate_or_run(sys.argv, sim)
def generate(ref="Example6_PyNN", add_inputs=True):
################################################################################
### Build new network
net = Network(id=ref, notes="Another network for PyNN - work in progress...")
net.parameters = {
"N_scaling": 0.005,
"layer_height": 400,
"width": 100,
"depth": 100,
"input_weight": 0.1,
}
cell = Cell(id="CorticalCell", pynn_cell="IF_curr_exp")
cell.parameters = {
"cm": 0.25, # nF
"i_offset": 0.0, # nA
"tau_m": 10.0, # ms
"tau_refrac": 2.0, # ms
"v_reset": -65.0, # mV
"v_rest": -65.0, # mV
"v_thresh": -50.0, # mV
}
net.cells.append(cell)
if add_inputs:
input_cell = Cell(id="InputCell", pynn_cell="SpikeSourcePoisson")
input_cell.parameters = {"start": 0, "duration": 10000000000, "rate": 150}
net.cells.append(input_cell)
e_syn = Synapse(
id="ampa",
pynn_receptor_type="excitatory",
pynn_synapse_type="curr_exp",
parameters={"tau_syn": 0.5},
)
net.synapses.append(e_syn)
i_syn = Synapse(
id="gaba",
pynn_receptor_type="inhibitory",
pynn_synapse_type="curr_exp",
parameters={"tau_syn": 0.5},
)
net.synapses.append(i_syn)
N_full = {
"L23": {"E": 20683, "I": 5834},
"L4": {"E": 21915, "I": 5479},
"L5": {"E": 4850, "I": 1065},
"L6": {"E": 14395, "I": 2948},
}
scale = 0.1
pops = []
input_pops = []
pop_dict = {}
layers = ["L23"]
layers = ["L23", "L4", "L5", "L6"]
for l in layers:
i = 3 - layers.index(l)
r = RectangularRegion(
id=l,
x=0,
y=i * net.parameters["layer_height"],
z=0,
width=net.parameters["width"],
height=net.parameters["layer_height"],
depth=net.parameters["depth"],
)
net.regions.append(r)
for t in ["E", "I"]:
try:
import opencortex.utils.color as occ
if l == "L23":
if t == "E":
color = occ.L23_PRINCIPAL_CELL
if t == "I":
color = occ.L23_INTERNEURON
if l == "L4":
if t == "E":
color = occ.L4_PRINCIPAL_CELL
if t == "I":
color = occ.L4_INTERNEURON
if l == "L5":
if t == "E":
color = occ.L5_PRINCIPAL_CELL
if t == "I":
color = occ.L5_INTERNEURON
if l == "L6":
if t == "E":
color = occ.L6_PRINCIPAL_CELL
if t == "I":
color = occ.L6_INTERNEURON
except:
color = ".8 0 0" if t == "E" else "0 0 1"
pop_id = "%s_%s" % (l, t)
pops.append(pop_id)
ref = "l%s%s" % (l[1:], t.lower())
exec(
ref
+ " = Population(id=pop_id, size='int(%s*N_scaling)'%N_full[l][t], component=cell.id, properties={'color':color, 'type':t})"
)
exec("%s.random_layout = RandomLayout(region = r.id)" % ref)
exec("net.populations.append(%s)" % ref)
exec("pop_dict['%s'] = %s" % (pop_id, ref))
if add_inputs:
color = ".8 .8 .8"
input_id = "%s_%s_input" % (l, t)
input_pops.append(input_id)
input_ref = "l%s%s_i" % (l[1:], t.lower())
exec(
input_ref
+ " = Population(id=input_id, size='int(%s*N_scaling)'%N_full[l][t], component=input_cell.id, properties={'color':color})"
)
exec("%s.random_layout = RandomLayout(region = r.id)" % input_ref)
exec("net.populations.append(%s)" % input_ref)
# l23i = Population(id='L23_I', size=int(100*scale), component=cell.id, properties={'color':})
# l23ei = Population(id='L23_E_input', size=int(100*scale), component=input_cell.id)
# l23ii = Population(id='L23_I_input', size=int(100*scale), component=input_cell.id)
# net.populations.append(l23e)
# net.populations.append(l23ei)
# net.populations.append(l23i)
# net.populations.append(l23ii)
conn_probs = [
[0.1009, 0.1689, 0.0437, 0.0818, 0.0323, 0.0, 0.0076, 0.0],
[0.1346, 0.1371, 0.0316, 0.0515, 0.0755, 0.0, 0.0042, 0.0],
[0.0077, 0.0059, 0.0497, 0.135, 0.0067, 0.0003, 0.0453, 0.0],
[0.0691, 0.0029, 0.0794, 0.1597, 0.0033, 0.0, 0.1057, 0.0],
[0.1004, 0.0622, 0.0505, 0.0057, 0.0831, 0.3726, 0.0204, 0.0],
[0.0548, 0.0269, 0.0257, 0.0022, 0.06, 0.3158, 0.0086, 0.0],
[0.0156, 0.0066, 0.0211, 0.0166, 0.0572, 0.0197, 0.0396, 0.2252],
[0.0364, 0.001, 0.0034, 0.0005, 0.0277, 0.008, 0.0658, 0.1443],
]
if add_inputs:
for p in pops:
proj = Projection(
id="proj_input_%s" % p,
presynaptic="%s_input" % p,
postsynaptic=p,
synapse=e_syn.id,
delay=2,
weight="input_weight",
)
proj.one_to_one_connector = OneToOneConnector()
net.projections.append(proj)
for pre_i in range(len(pops)):
for post_i in range(len(pops)):
pre = pops[pre_i]
post = pops[post_i]
prob = conn_probs[post_i][pre_i] ####### TODO: check!!!!
weight = 1
syn = e_syn
if prob > 0:
if "I" in pre:
weight = -1
syn = i_syn
proj = Projection(
id="proj_%s_%s" % (pre, post),
presynaptic=pre,
postsynaptic=post,
synapse=syn.id,
delay=1,
weight=weight,
)
proj.random_connectivity = RandomConnectivity(probability=prob)
net.projections.append(proj)
print(net.to_json())
new_file = net.to_json_file("%s.json" % net.id)
################################################################################
### Build Simulation object & save as JSON
record_traces = {}
record_spikes = {}
from neuromllite.utils import evaluate
for p in pops:
forecast_size = evaluate(pop_dict[p].size, net.parameters)
record_traces[p] = list(range(min(2, forecast_size)))
record_spikes[p] = "*"
for ip in input_pops:
record_spikes[ip] = "*"
sim = Simulation(
id="Sim%s" % net.id,
network=new_file,
duration="100",
dt="0.025",
seed=1234,
record_traces=record_traces,
record_spikes=record_spikes,
)
sim.to_json_file()
return sim, net