from neuromllite import Network, Cell, InputSource, Population, Synapse, RectangularRegion, RandomLayout
from neuromllite import Projection, RandomConnectivity, Input, Simulation
import sys
################################################################################
### Build new network
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)
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(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
from neuromllite import Network, Cell, InputSource, Population, Synapse
from neuromllite import Projection, RandomConnectivity, Input, Simulation
import sys
################################################################################
### 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)
Population,
Synapse,
RectangularRegion,
RandomLayout,
)
from neuromllite import Projection, RandomConnectivity, Input, Simulation
import sys
################################################################################
### Build new network
net = Network(id="SonataExample")
net.notes = "Example for testing Sonata"
net.parameters = {"input_amp": 0.190, "input_del": 100, "input_dur": 800}
cell = Cell(id="testcell", pynn_cell="IF_cond_alpha")
cell.parameters = {
"i_offset": 0,
"cm": 0.117,
"tau_m": 22.1,
"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)"""
from neuromllite import Network, Cell, InputSource, Population, Synapse
from neuromllite import Projection, RandomConnectivity, Input, Simulation
import sys
################################################################################
### Build new network
net = Network(id='Example9_HindmarshRose')
net.notes = 'Example 9: HindmarshRose'
net.parameters = {'N': 1}
cell = Cell(id='hrCell', lems_source_file='test_files/HindmarshRose3d.xml')
cell.parameters = {}
params = {
'a': 1,
'b': 3,
'c': -3.0,
'd': 5.0,
's': 4.0,
'I': 5.0,
'x1': -1.3,
'r': 0.002,
'x0': -1.3,
'y0': -1.0,
'z0': 1.0
}
for p in params:
cell.parameters[p] = p
net.parameters[p] = params[p]
from neuromllite import Network, Cell, InputSource, Population, Synapse
from neuromllite import Projection, RandomConnectivity, Input, Simulation
import sys
################################################################################
### Build new network
net = Network(id="Spikers")
net.notes = "Example with spiking entities.."
net.parameters = {"N": 10, "weightInput": 10, "input_rate": 40}
cell = Cell(id="iafcell", pynn_cell="IF_cond_alpha")
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)
from neuromllite import Network, Cell, InputSource, Population, Synapse
from neuromllite import Projection, RandomConnectivity, Input, Simulation
import sys
################################################################################
### Build new network
net = Network(id='Example10_Lorenz')
net.notes = 'Example 10: Lorenz'
net.parameters = {'N': 1}
cell = Cell(id='lorenzCell',
lems_source_file='test_files/Lorenz1963.xml',
parameters={})
net.cells.append(cell)
params = {'sigma': 10, 'b': 2.67, 'r': 28, 'x0': 1.0, 'y0': 1.0, 'z0': 1.0}
for p in params:
cell.parameters[p] = p
net.parameters[p] = params[p]
pop = Population(id='lorenzPop',
size='1',
component=cell.id,
properties={'color': '.7 0 0'})
net.populations.append(pop)
print(net)
print(net.to_json())
new_file = net.to_json_file('%s.json' % net.id)
id="region2", x=1000, y=1000, z=1000, width=1000, height=100, depth=1000
)
net.regions.append(r1)
net.regions.append(r2)
net.populations[0].size = 1
net.populations[0].relative_layout = RelativeLayout(x=0, y=50, z=0, region=r1.id)
net.populations[1].size = 1
net.populations[1].relative_layout = RelativeLayout(x=50, y=0, z=50, region=r2.id)
net.populations[0].component = "hhcell"
net.populations[1].component = "hhcell"
net.cells.append(
Cell(
id="hhcell", neuroml2_source_file="../../../examples/test_files/hhcell.cell.nml"
)
)
net.synapses.append(
Synapse(
id="ampa", neuroml2_source_file="../../../examples/test_files/ampa.synapse.nml"
)
)
print(net.to_json())
new_file = net.to_json_file("%s.json" % net.id)
################################################################################
### Use a handler which just prints info on positions, etc.
def 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
from neuromllite import Network, Cell, InputSource, Population, Synapse
from neuromllite import Projection, RandomConnectivity, Input, Simulation
import sys
################################################################################
### Build new network
net = Network(id='Spikers')
net.notes = 'Example with spiking entities..'
net.parameters = { 'N': 10, 'weightInput': 10, 'input_rate': 40}
cell = Cell(id='iafcell', pynn_cell='IF_cond_alpha')
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)
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
from neuromllite import Network, Cell, InputSource, Population, Synapse, RectangularRegion, RandomLayout
from neuromllite import Projection, RandomConnectivity, Input, Simulation
import sys
################################################################################
### 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.,
'stop': 800.
})
net.input_sources.append(input_source)
r1 = RectangularRegion(id='region1',
x=0,
y=0,
z=0,
width=1000,
height=100,
################################################################################
### Build new network
net = Network(id="ArborExample")
net.notes = "Example for testing Arbor"
net.parameters = {
"v_init": -50,
"scale": 3,
"input_amp": 0.01,
"input_del": 50,
"input_dur": 5,
}
cell = Cell(id="test_arbor_cell", arbor_cell="cable_cell")
cell.parameters = {"v_init": "v_init", "radius": 3, "mechanism": "hh"}
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",
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 = 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():
################################################################################
### 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
from neuromllite import Network, Cell, InputSource, Population, Synapse
from neuromllite import Projection, RandomConnectivity, Input, Simulation
import sys
################################################################################
### Build new network
net = Network(id='IzhikevichTest')
net.notes = 'Example Izhikevich'
net.parameters = {'N': 1}
cell = Cell(id='izhCell', neuroml2_cell='izhikevich2007Cell')
cell.parameters = {}
params = {'v0':'-60mV', 'C':'100 pF', 'k':'0.7 nS_per_mV', \
'vr':'-60 mV', 'vt':'-40 mV', 'vpeak':'35 mV', \
'a':'0.03 per_ms', 'b':'-2 nS', 'c':'-50 mV', 'd':'100 pA'}
for p in params:
cell.parameters[p] = p
net.parameters[p] = params[p]
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'
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
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,
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
# Build the network
net = Network(id='WC')
net.notes = 'A simple WC network'
net.parameters = {'wee': 10, 'wei': 12, 'wie': -8, 'wii': -3}
r1 = RectangularRegion(id='WilsonCowan',
x=0,
y=0,
z=0,
width=1000,
height=100,
depth=1000)
net.regions.append(r1)
exc_cell = Cell(id='Exc', lems_source_file='WC_Parameters.xml')
inh_cell = Cell(id='Inh', lems_source_file='WC_Parameters.xml')
net.cells.append(exc_cell)
net.cells.append(inh_cell)
exc_pop = Population(id='Excitatory',
size=1,
component=exc_cell.id,
properties={
'color': '0.8 0 0',
'radius': 10
},
relative_layout=RelativeLayout(region=r1.id,
x=-20,
y=0,
z=0))
from neuromllite import Network, Cell, Synapse, NetworkReader, InputSource, Input
from neuromllite.NetworkGenerator import generate_neuroml2_from_network
import sys
################################################################################
### Build new network
percent = 5
net = Network(
id="BBP_%spercent" % percent,
notes=
"A network with the Blue Brain Project connectivity data (%s% of total cells)",
)
default_cell = Cell(id="hhcell",
neuroml2_source_file="test_files/hhcell.cell.nml")
net.network_reader = NetworkReader(
type="BBPConnectomeReader",
parameters={
"id": net.id,
"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)
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 = 0.05
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
from neuromllite import Network, Cell, InputSource, Population, Synapse, RectangularRegion, RandomLayout
from neuromllite import Projection, RandomConnectivity, Input, Simulation
import sys
################################################################################
### Build new network
net = Network(id='SonataExample')
net.notes = 'Example for testing Sonata'
net.parameters = {'input_amp': 0.190, 'input_del': 100, 'input_dur': 800}
cell = Cell(id='testcell', pynn_cell='IF_cond_alpha')
cell.parameters = {
"i_offset": 0,
"cm": 0.117,
"tau_m": 22.1,
"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={
Synapse,
RectangularRegion,
RandomLayout,
)
from neuromllite import Projection, RandomConnectivity, Input, Simulation
import sys
################################################################################
### 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",
# conn=flnMatp['flnMatpython'][:][:] #fln values..Cij is strength from j to i
# conn=conn[:nAreas,:nAreas]
# distMatp = scipy.io.loadmat(path+'subgraphWiring29.mat')
# distMat=distMatp['wiring'][:][:] #distances between areas values..
# delayMat = distMat/speed
# delay=delayMat[:nAreas,:nAreas]
###################### Build the network ######################################
net = Network(id='Joglekar1Network_PyNN')
net.notes = 'Joglekar network: a network with PyNN cells & inputs'
net.parameters = {'scale': 0.01}
######################## Cell #################################################
cellE = Cell(id='excitatory', pynn_cell='IF_curr_alpha')
cellI = Cell(id='inhibitory', pynn_cell='IF_curr_alpha')
cellE.parameters = {
"tau_m": tauE_m,
"cm": cE_m,
"v_rest": Vrest,
"v_reset": Vreset,
"v_thresh": Vt,
"tau_refrac": tauRef,
"i_offset": (VextE / R)
}
cellI.parameters = {
"tau_m": tauI_m,
"cm": cI_m,
from neuromllite import Network, Cell, InputSource, Population, Synapse
from neuromllite import Projection, RandomConnectivity, Input, Simulation
import sys
################################################################################
### Build new network
net = Network(id="Example9_HindmarshRose")
net.notes = "Example 9: HindmarshRose"
net.parameters = {"N": 1}
cell = Cell(id="hrCell", lems_source_file="test_files/HindmarshRose3d.xml")
cell.parameters = {}
params = {
"a": 1,
"b": 3,
"c": -3.0,
"d": 5.0,
"s": 4.0,
"I": 5.0,
"x1": -1.3,
"r": 0.002,
"x0": -1.3,
"y0": -1.0,
"z0": 1.0,
}
for p in params:
cell.parameters[p] = p
net.parameters[p] = params[p]