"tau_refrac": 3,
"v_reset": -50,
"v_rest": -78,
"v_thresh": -47
}
net.cells.append(cell)
'''
cell2 = Cell(id='testcell2', pynn_cell='IF_cond_alpha')
cell2.parameters = { "tau_refrac":5, "i_offset":-.1 }
net.cells.append(cell2)'''
input_source = InputSource(id='i_clamp',
pynn_input='DCSource',
parameters={
'amplitude': 'input_amp',
'start': 'input_del',
'stop': 'input_del+input_dur'
})
net.input_sources.append(input_source)
r1 = RectangularRegion(id='region1',
x=0,
y=0,
z=0,
width=1000,
height=100,
depth=1000)
net.regions.append(r1)
p0 = Population(id='pop0',
"v_reset": -50,
"v_rest": -78,
"v_thresh": -47,
}
net.cells.append(cell)
"""
cell2 = Cell(id='testcell2', pynn_cell='IF_cond_alpha')
cell2.parameters = { "tau_refrac":5, "i_offset":-.1 }
net.cells.append(cell2)"""
input_source = InputSource(
id="i_clamp",
pynn_input="DCSource",
parameters={
"amplitude": "input_amp",
"start": "input_del",
"stop": "input_del+input_dur",
},
)
net.input_sources.append(input_source)
r1 = RectangularRegion(id="region1",
x=0,
y=0,
z=0,
width=1000,
height=100,
depth=1000)
net.regions.append(r1)
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
### Build new network
net = Network(id='PopExample')
net.notes = 'Testing...'
net.seed = 1234
net.parameters = { 'N': 10, 'fractionE': 0.8, 'weightInput': 1, 'Wei':0.01, 'Wie':0.01}
cell = Cell(id='iafcell', pynn_cell='IF_cond_alpha')
cell.parameters = { "tau_refrac":0}
net.cells.append(cell)
input_source = InputSource(id='poissonFiringSyn100Hz', neuroml2_source_file='inputs.nml')
net.input_sources.append(input_source)
net.input_sources.append(input_source)
pE = Population(id='Epop', size='int(N*fractionE)', component=cell.id, properties={'color':'.7 0 0'})
pI = Population(id='Ipop', size='N - int(N*fractionE)', component=cell.id, properties={'color':'0 0 .7'})
net.populations.append(pE)
net.populations.append(pI)
net.synapses.append(Synapse(id='ampaSyn',
pynn_receptor_type='excitatory',
pynn_synapse_type='cond_alpha',
pops = [exc_pop, inh_pop]
internal_connections(pops)
# Add offset inputs
net.parameters['exc_input'] = '0nA'
net.parameters['inh_input'] = '0nA'
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',
################################################################################
### Build new network
net = Network(id='delMolinoEtAl')
net.notes = 'delMolinoEtAl eLife 2017'
net.parameters = {'inputVIP': 10}
cell = Cell(id='ifcell', pynn_cell='IF_cond_alpha')
cell.parameters = {"tau_refrac": 5, "i_offset": .1}
net.cells.append(cell)
input_source0 = InputSource(id='iclamp0',
pynn_input='DCSource',
parameters={
'amplitude': 10,
'start': 50.,
'stop': 150.
})
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)
from neuromllite import Network, Cell, InputSource, Population, Synapse
from neuromllite import Projection, RandomConnectivity, Input, Simulation
import sys
################################################################################
### Build new network
net = Network(id='Example8_Extension')
net.notes = 'Example 8: extending exising networks'
net.parameters = {'N': 10, 'fractionE': 0.8, 'weightInput': 1}
cell = Cell(id='hhcell', neuroml2_source_file='test_files/hhcell.cell.nml')
net.cells.append(cell)
input_source = InputSource(id='poissonFiringSyn',
neuroml2_source_file='test_files/inputs.nml')
net.input_sources.append(input_source)
'''
input_source = InputSource(id='iclamp0',
pynn_input='DCSource',
parameters={'amplitude':0.2, 'start':100., 'stop':900.})'''
net.input_sources.append(input_source)
pE = Population(id='Epop',
size='int(N*fractionE)',
component=cell.id,
properties={'color': '.7 0 0'})
pRS = Population(id='RSpop',
size='N - int(N*fractionE)',
component=cell.id,
### Build new network
net = Network(id="Example11_Synapses")
net.notes = "Example 11: synaptic properties"
net.parameters = {"input_amp": 0.23, "weight": 1.01}
#'tau_syn': 2}
cell = Cell(id="iafCell0", neuroml2_source_file="test_files/iaf.cell.nml")
# cell.parameters = { "tau_refrac":5, "i_offset":0 }
net.cells.append(cell)
input_source = InputSource(
id="i_clamp",
pynn_input="DCSource",
parameters={
"amplitude": "input_amp",
"start": 200.0,
"stop": 800.0
},
)
net.input_sources.append(input_source)
r1 = RectangularRegion(id="region1",
x=0,
y=0,
z=0,
width=1000,
height=100,
depth=1000)
net.regions.append(r1)
net.cells.append(cell)
pop = Population(id='izhPop',
size='1',
component=cell.id,
properties={'color': '.7 0 0'})
net.populations.append(pop)
net.parameters['delay'] = '100ms'
net.parameters['stim_amp'] = '100pA'
net.parameters['duration'] = '500ms'
input_source = InputSource(id='iclamp_0',
neuroml2_input='PulseGenerator',
parameters={
'amplitude': 'stim_amp',
'delay': 'delay',
'duration': 'duration'
})
net.input_sources.append(input_source)
net.inputs.append(
Input(id='stim',
input_source=input_source.id,
population=pop.id,
percentage=100))
print(net)
print(net.to_json())
new_file = net.to_json_file('%s.json' % net.id)
net = Network(id='Example4_PyNN')
net.notes = 'Example 4: a network with PyNN cells & inputs'
net.parameters = {'input_amp': 0.99}
cell = Cell(id='testcell', pynn_cell='IF_cond_alpha')
cell.parameters = {"tau_refrac": 5, "i_offset": .1}
net.cells.append(cell)
cell2 = Cell(id='testcell2', pynn_cell='IF_cond_alpha')
cell2.parameters = {"tau_refrac": 5, "i_offset": -.1}
net.cells.append(cell2)
input_source = InputSource(id='i_clamp',
pynn_input='DCSource',
parameters={
'amplitude': 'input_amp',
'start': 200.,
'stop': 800.
})
net.input_sources.append(input_source)
r1 = RectangularRegion(id='region1',
x=0,
y=0,
z=0,
width=1000,
height=100,
depth=1000)
net.regions.append(r1)
p0 = Population(id='pop0',
"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
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
'start': 0,
'duration': 10000000000,
'rate': 'input_rate'
}
net.cells.append(input_cell)
input_cell_100 = Cell(id='InputCell100', pynn_cell='SpikeSourcePoisson')
input_cell_100.parameters = {
'start': 0,
'duration': 10000000000,
'rate': 100
}
net.cells.append(input_cell_100)
input_source_p0 = InputSource(id='poissonFiringSyn', neuroml2_source_file='../test_files/inputs.nml')
net.input_sources.append(input_source_p0)
input_source1 = InputSource(id='iclamp1',
pynn_input='DCSource',
parameters={'amplitude':0.8, 'start':100., 'stop':900.})
net.input_sources.append(input_source1)
pop0 = Population(id='pop0', size='N', component=cell.id, properties={'color':'.7 0 0'})
net.populations.append(pop0)
pop1 = Population(id='pop1', size='N', component=cell.id, properties={'color':'0 .7 0'})
net.populations.append(pop1)
pop2 = Population(id='pop2', size='N', component=cell.id, properties={'color':'0 .7 0.7'})
net.populations.append(pop2)
net = Network(id='Syn4Net')
net.notes = 'Syn4Net: synaptic properties'
net.parameters = { 'weight': 0.001,
'stim1_delay': 50,
'stim2_delay': 140}
cell = Cell(id='passiveCell', neuroml2_source_file='passiveCell.cell.nml')
net.cells.append(cell)
spkArr1 = Cell(id='spkArr1', neuroml2_source_file='inputs.nml')
net.cells.append(spkArr1)
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)
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
cell.parameters = {"tau_refrac": 10}
net.cells.append(cell)
input_cell = Cell(id="InputCell", pynn_cell="SpikeSourcePoisson")
input_cell.parameters = {
"start": 0,
"duration": 10000000000,
"rate": "input_rate"
}
net.cells.append(input_cell)
input_cell_100 = Cell(id="InputCell100", pynn_cell="SpikeSourcePoisson")
input_cell_100.parameters = {"start": 0, "duration": 10000000000, "rate": 100}
net.cells.append(input_cell_100)
input_source_p0 = InputSource(id="poissonFiringSyn",
neuroml2_source_file="../test_files/inputs.nml")
net.input_sources.append(input_source_p0)
input_source1 = InputSource(
id="iclamp1",
pynn_input="DCSource",
parameters={
"amplitude": 0.8,
"start": 100.0,
"stop": 900.0
},
)
net.input_sources.append(input_source1)
pop0 = Population(id="pop0",
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',
lems_source_file='TestNCS.xml',
parameters={
'mean': '0.0nA',
'stdev': str(stdNoiseE) + 'nA',
'noiseDt': '%sms' % dt,
'delay': '0ms',
'duration': '%sms' % duration
})
# Noise on inhibitory neurons
stdNoiseI = (sigmaV / R) * (tauI_m**0.5) / (dt**0.5)
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
})
properties={
'color': '0 0 0.8',
'radius': 10
},
relative_layout=RelativeLayout(region=r1.id,
x=20,
y=0,
z=0))
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)