def __init__(self, sim, num_threads, parameters):
Model.__init__(self, sim, num_threads, parameters)
# Load components
CortexExcL4 = load_component(self.parameters.l4_cortex_exc.component)
CortexInhL4 = load_component(self.parameters.l4_cortex_inh.component)
RetinaLGN = load_component(self.parameters.retina_lgn.component)
# Build and instrument the network
self.visual_field = VisualRegion(location_x=self.parameters.visual_field.centre[0],location_y=self.parameters.visual_field.centre[1],size_x=self.parameters.visual_field.size[0],size_y=self.parameters.visual_field.size[1])
self.input_layer = RetinaLGN(self, self.parameters.retina_lgn.params)
cortex_exc_l4 = CortexExcL4(self, self.parameters.l4_cortex_exc.params)
cortex_inh_l4 = CortexInhL4(self, self.parameters.l4_cortex_inh.params)
GaborConnector(self,self.input_layer.sheets['X_ON'],self.input_layer.sheets['X_OFF'],cortex_exc_l4,self.parameters.l4_cortex_exc.AfferentConnection,'V1AffConnection')
GaborConnector(self,self.input_layer.sheets['X_ON'],self.input_layer.sheets['X_OFF'],cortex_inh_l4,self.parameters.l4_cortex_inh.AfferentConnection,'V1AffInhConnection')
# initialize projections
ModularSingleWeightProbabilisticConnector(self,'V1L4ExcL4ExcConnectionRand',cortex_exc_l4,cortex_exc_l4,self.parameters.l4_cortex_exc.L4ExcL4ExcConnectionRand).connect()
ModularSingleWeightProbabilisticConnector(self,'V1L4ExcL4InhConnectionRand',cortex_exc_l4,cortex_inh_l4,self.parameters.l4_cortex_exc.L4ExcL4InhConnectionRand).connect()
ModularSingleWeightProbabilisticConnector(self,'V1L4InhL4ExcConnectionRand',cortex_inh_l4,cortex_exc_l4,self.parameters.l4_cortex_inh.L4InhL4ExcConnectionRand).connect()
ModularSingleWeightProbabilisticConnector(self,'V1L4InhL4InhConnectionRand',cortex_inh_l4,cortex_inh_l4,self.parameters.l4_cortex_inh.L4InhL4InhConnectionRand).connect()
# initialize projections
ModularSamplingProbabilisticConnector(self,'V1L4ExcL4ExcConnection',cortex_exc_l4,cortex_exc_l4,self.parameters.l4_cortex_exc.L4ExcL4ExcConnection).connect()
ModularSamplingProbabilisticConnector(self,'V1L4ExcL4InhConnection',cortex_exc_l4,cortex_inh_l4,self.parameters.l4_cortex_exc.L4ExcL4InhConnection).connect()
ModularSamplingProbabilisticConnector(self,'V1L4InhL4ExcConnection',cortex_inh_l4,cortex_exc_l4,self.parameters.l4_cortex_inh.L4InhL4ExcConnection).connect()
ModularSamplingProbabilisticConnector(self,'V1L4InhL4InhConnection',cortex_inh_l4,cortex_inh_l4,self.parameters.l4_cortex_inh.L4InhL4InhConnection).connect()
def __init__(self, sim, num_threads, parameters):
Model.__init__(self, sim, num_threads, parameters)
# Load components
RetinaLGN = load_component(self.parameters.retina_lgn.component)
PGN = load_component(self.parameters.pgn.component)
# Build and instrument the network
self.visual_field = VisualRegion(
location_x=self.parameters.visual_field.centre[0],
location_y=self.parameters.visual_field.centre[1],
size_x=self.parameters.visual_field.size[0],
size_y=self.parameters.visual_field.size[1])
# init layers
self.input_layer = RetinaLGN(self, self.parameters.retina_lgn.params)
pgn = PGN(self, self.parameters.pgn.params)
########################################################
# LGN-PGN
ModularSamplingProbabilisticConnector(
self,
'LGN_PGN_ConnectionOn', # name
self.input_layer.sheets['X_ON'], # source
pgn, # target
self.parameters.pgn.LGN_PGN_ConnectionOn # params
).connect()
ModularSamplingProbabilisticConnector(
self,
'LGN_PGN_ConnectionOff', # name
self.input_layer.sheets['X_OFF'], # source
pgn, # target
self.parameters.pgn.LGN_PGN_ConnectionOff # params
).connect()
ModularSamplingProbabilisticConnector(
self,
'PGN_PGN_Connection', # name
pgn, # source
pgn, # target
self.parameters.pgn.PGN_PGN_Connection # params
).connect()
ModularSamplingProbabilisticConnector(
self,
'PGN_LGN_ConnectionOn', # name
pgn, # source
self.input_layer.sheets['X_ON'], # target
self.parameters.pgn.PGN_LGN_ConnectionOn # params
).connect()
ModularSamplingProbabilisticConnector(
self,
'PGN_LGN_ConnectionOff', # name
pgn, # source
self.input_layer.sheets['X_OFF'], # target
self.parameters.pgn.PGN_LGN_ConnectionOff # params
).connect()
def __init__(self, sim, num_threads, parameters):
Model.__init__(self, sim, num_threads, parameters)
# Load components
CortexExcL4 = load_component(
self.parameters.sheets.l4_cortex_exc.component)
CortexInhL4 = load_component(
self.parameters.sheets.l4_cortex_inh.component)
if not self.parameters.only_afferent and self.parameters.l23:
CortexExcL23 = load_component(
self.parameters.sheets.l23_cortex_exc.component)
CortexInhL23 = load_component(
self.parameters.sheets.l23_cortex_inh.component)
RetinaLGN = load_component(self.parameters.sheets.retina_lgn.component)
# Build and instrument the network
self.visual_field = VisualRegion(
location_x=self.parameters.visual_field.centre[0],
location_y=self.parameters.visual_field.centre[1],
size_x=self.parameters.visual_field.size[0],
size_y=self.parameters.visual_field.size[1],
)
self.input_layer = RetinaLGN(self,
self.parameters.sheets.retina_lgn.params)
cortex_exc_l4 = CortexExcL4(
self, self.parameters.sheets.l4_cortex_exc.params)
cortex_inh_l4 = CortexInhL4(
self, self.parameters.sheets.l4_cortex_inh.params)
if not self.parameters.only_afferent and self.parameters.l23:
cortex_exc_l23 = CortexExcL23(
self, self.parameters.sheets.l23_cortex_exc.params)
cortex_inh_l23 = CortexInhL23(
self, self.parameters.sheets.l23_cortex_inh.params)
# initialize afferent layer 4 projections
GaborConnector(
self,
self.input_layer.sheets["X_ON"],
self.input_layer.sheets["X_OFF"],
cortex_exc_l4,
self.parameters.sheets.l4_cortex_exc.AfferentConnection,
"V1AffConnection",
)
GaborConnector(
self,
self.input_layer.sheets["X_ON"],
self.input_layer.sheets["X_OFF"],
cortex_inh_l4,
self.parameters.sheets.l4_cortex_inh.AfferentConnection,
"V1AffInhConnection",
)
# initialize lateral layer 4 projections
if not self.parameters.only_afferent:
ModularSamplingProbabilisticConnectorAnnotationSamplesCount(
self,
"V1L4ExcL4ExcConnection",
cortex_exc_l4,
cortex_exc_l4,
self.parameters.sheets.l4_cortex_exc.L4ExcL4ExcConnection,
).connect()
ModularSamplingProbabilisticConnectorAnnotationSamplesCount(
self,
"V1L4ExcL4InhConnection",
cortex_exc_l4,
cortex_inh_l4,
self.parameters.sheets.l4_cortex_exc.L4ExcL4InhConnection,
).connect()
ModularSamplingProbabilisticConnector(
self,
"V1L4InhL4ExcConnection",
cortex_inh_l4,
cortex_exc_l4,
self.parameters.sheets.l4_cortex_inh.L4InhL4ExcConnection,
).connect()
ModularSamplingProbabilisticConnector(
self,
"V1L4InhL4InhConnection",
cortex_inh_l4,
cortex_inh_l4,
self.parameters.sheets.l4_cortex_inh.L4InhL4InhConnection,
).connect()
if self.parameters.l23:
# initialize afferent layer 4 to layer 2/3 projection
ModularSamplingProbabilisticConnector(
self,
"V1L4ExcL23ExcConnection",
cortex_exc_l4,
cortex_exc_l23,
self.parameters.sheets.l23_cortex_exc.
L4ExcL23ExcConnection,
).connect()
ModularSamplingProbabilisticConnector(
self,
"V1L4ExcL23InhConnection",
cortex_exc_l4,
cortex_inh_l23,
self.parameters.sheets.l23_cortex_inh.
L4ExcL23InhConnection,
).connect()
ModularSamplingProbabilisticConnector(
self,
"V1L23ExcL23ExcConnection",
cortex_exc_l23,
cortex_exc_l23,
self.parameters.sheets.l23_cortex_exc.
L23ExcL23ExcConnection,
).connect()
ModularSamplingProbabilisticConnector(
self,
"V1L23ExcL23InhConnection",
cortex_exc_l23,
cortex_inh_l23,
self.parameters.sheets.l23_cortex_exc.
L23ExcL23InhConnection,
).connect()
ModularSamplingProbabilisticConnector(
self,
"V1L23InhL23ExcConnection",
cortex_inh_l23,
cortex_exc_l23,
self.parameters.sheets.l23_cortex_inh.
L23InhL23ExcConnection,
).connect()
ModularSamplingProbabilisticConnector(
self,
"V1L23InhL23InhConnection",
cortex_inh_l23,
cortex_inh_l23,
self.parameters.sheets.l23_cortex_inh.
L23InhL23InhConnection,
).connect()
if self.parameters.feedback:
ModularSamplingProbabilisticConnector(
self,
"V1L23ExcL4ExcConnection",
cortex_exc_l23,
cortex_exc_l4,
self.parameters.sheets.l23_cortex_exc.
L23ExcL4ExcConnection,
).connect()
ModularSamplingProbabilisticConnector(
self,
"V1L23ExcL4InhConnection",
cortex_exc_l23,
cortex_inh_l4,
self.parameters.sheets.l23_cortex_exc.
L23ExcL4InhConnection,
).connect()
def __init__(self, sim, num_threads, parameters):
Model.__init__(self, sim, num_threads, parameters)
# Load components
LGN = load_component(self.parameters.lgn.component)
# Instance
self.input_layer = LGN(self, self.parameters.lgn.params)
# Build and instrument the network
self.visual_field = VisualRegion(
location_x=self.parameters.visual_field.centre[0],
location_y=self.parameters.visual_field.centre[1],
size_x=self.parameters.visual_field.size[0],
size_y=self.parameters.visual_field.size[1]
)
# PROJECTIONS
########################################################
# PGN
if withPGN:
# Load components
PGN = load_component( self.parameters.pgn.component )
# Instance
pgn = PGN(self, self.parameters.pgn.params)
# LGN-PGN
ModularSamplingProbabilisticConnector(
self,
'LGN_PGN_ConnectionOn', # name
self.input_layer.sheets['X_ON'], # source
pgn, # target
self.parameters.pgn.LGN_PGN_ConnectionOn # params
).connect()
ModularSamplingProbabilisticConnector(
self,
'LGN_PGN_ConnectionOff', # name
self.input_layer.sheets['X_OFF'], # source
pgn, # target
self.parameters.pgn.LGN_PGN_ConnectionOff # params
).connect()
ModularSamplingProbabilisticConnector(
self,
'PGN_PGN_Connection', # name
pgn, # source
pgn, # target
self.parameters.pgn.PGN_PGN_Connection # params
).connect()
ModularSamplingProbabilisticConnector(
self,
'PGN_LGN_ConnectionOn', # name
pgn, # source
self.input_layer.sheets['X_ON'], # target
self.parameters.pgn.PGN_LGN_ConnectionOn # params
).connect()
ModularSamplingProbabilisticConnector(
self,
'PGN_LGN_ConnectionOff', # name
pgn, # source
self.input_layer.sheets['X_OFF'], # target
self.parameters.pgn.PGN_LGN_ConnectionOff # params
).connect()
# V1
if withV1: # CTC
# Load components
CortexExcL4 = load_component(self.parameters.l4_cortex_exc.component)
CortexInhL4 = load_component(self.parameters.l4_cortex_inh.component)
# Instance
cortex_exc_l4 = CortexExcL4(self, self.parameters.l4_cortex_exc.params)
cortex_inh_l4 = CortexInhL4(self, self.parameters.l4_cortex_inh.params)
# ########################################################
# THALAMO-CORTICAL
# initialize afferent layer 4 projections
GaborConnector(
self,
self.input_layer.sheets['X_ON'],
self.input_layer.sheets['X_OFF'],
cortex_exc_l4, # target
self.parameters.l4_cortex_exc.AfferentConnection, # parameters
'V1AffConnection' # name
)
GaborConnector(
self,
self.input_layer.sheets['X_ON'],
self.input_layer.sheets['X_OFF'],
cortex_inh_l4,
self.parameters.l4_cortex_inh.AfferentConnection,
'V1AffInhConnection'
)
# ########################################################
# CORTICO-CORTICAL
# random lateral layer 4 projections
ModularSingleWeightProbabilisticConnector(
self,
'V1L4ExcL4ExcConnectionRand',
cortex_exc_l4,
cortex_exc_l4,
self.parameters.l4_cortex_exc.L4ExcL4ExcConnectionRand
).connect()
ModularSingleWeightProbabilisticConnector(
self,
'V1L4ExcL4InhConnectionRand',
cortex_exc_l4,
cortex_inh_l4,
self.parameters.l4_cortex_exc.L4ExcL4InhConnectionRand
).connect()
ModularSingleWeightProbabilisticConnector(
self,
'V1L4InhL4ExcConnectionRand',
cortex_inh_l4,
cortex_exc_l4,
self.parameters.l4_cortex_inh.L4InhL4ExcConnectionRand
).connect()
ModularSingleWeightProbabilisticConnector(
self,
'V1L4InhL4InhConnectionRand',
cortex_inh_l4,
cortex_inh_l4,
self.parameters.l4_cortex_inh.L4InhL4InhConnectionRand
).connect()
# lateral layer 4 projections
ModularSamplingProbabilisticConnector(
self,
'V1L4ExcL4ExcConnection',
cortex_exc_l4,
cortex_exc_l4,
self.parameters.l4_cortex_exc.L4ExcL4ExcConnection
).connect()
ModularSamplingProbabilisticConnector(
self,
'V1L4ExcL4InhConnection',
cortex_exc_l4,
cortex_inh_l4,
self.parameters.l4_cortex_exc.L4ExcL4InhConnection
).connect()
ModularSamplingProbabilisticConnector(
self,
'V1L4InhL4ExcConnection',
cortex_inh_l4,
cortex_exc_l4,
self.parameters.l4_cortex_inh.L4InhL4ExcConnection
).connect()
ModularSamplingProbabilisticConnector(
self,
'V1L4InhL4InhConnection',
cortex_inh_l4,
cortex_inh_l4,
self.parameters.l4_cortex_inh.L4InhL4InhConnection
).connect()
########################################################
# CORTICO-THALAMIC
if withFeedback_CxLGN:
ModularSamplingProbabilisticConnector(
self,
'V1EffConnectionOn',
cortex_exc_l4,
self.input_layer.sheets['X_ON'],
self.parameters.l4_cortex_exc.EfferentConnection_LGN
).connect()
ModularSamplingProbabilisticConnector(
self,
'V1EffConnectionOff',
cortex_exc_l4,
self.input_layer.sheets['X_OFF'],
self.parameters.l4_cortex_exc.EfferentConnection_LGN
).connect()
# GaborConnector(
# self,
# self.input_layer.sheets['X_ON'],
# self.input_layer.sheets['X_OFF'],
# cortex_exc_l4, # source
# self.parameters.l4_cortex_exc.EfferentConnection, # parameters
# 'V1EffConnection' # name
# )
if withFeedback_CxPGN and withPGN:
ModularSamplingProbabilisticConnector(
self,
'V1EffConnectionPGN',
cortex_exc_l4,
pgn,
self.parameters.l4_cortex_exc.EfferentConnection_PGN
).connect()
def __init__(self, sim, num_threads, parameters):
Model.__init__(self, sim, num_threads, parameters)
# Load components
CortexExcL4 = load_component(
self.parameters.sheets.l4_cortex_exc.component)
CortexInhL4 = load_component(
self.parameters.sheets.l4_cortex_inh.component)
if not self.parameters.only_afferent and self.parameters.l23:
CortexExcL23 = load_component(
self.parameters.sheets.l23_cortex_exc.component)
CortexInhL23 = load_component(
self.parameters.sheets.l23_cortex_inh.component)
RetinaLGN = load_component(self.parameters.sheets.retina_lgn.component)
# Build and instrument the network
self.visual_field = VisualRegion(
location_x=self.parameters.visual_field.centre[0],
location_y=self.parameters.visual_field.centre[1],
size_x=self.parameters.visual_field.size[0],
size_y=self.parameters.visual_field.size[1])
self.input_layer = RetinaLGN(
self, self.parameters.sheets.retina_lgn.params
) # 'pyNN.spiNNaker' has no attribute 'StepCurrentSource'
cortex_exc_l4 = CortexExcL4(
self, self.parameters.sheets.l4_cortex_exc.params
) # spiNNaker has no attribute EIF_cond_exp_isfa_ista ->Iz
cortex_inh_l4 = CortexInhL4(
self, self.parameters.sheets.l4_cortex_inh.params)
if not self.parameters.only_afferent and self.parameters.l23:
cortex_exc_l23 = CortexExcL23(
self, self.parameters.sheets.l23_cortex_exc.params)
cortex_inh_l23 = CortexInhL23(
self, self.parameters.sheets.l23_cortex_inh.params)
# initialize afferent layer 4 projections
GaborConnector(self, self.input_layer.sheets['X_ON'],
self.input_layer.sheets['X_OFF'], cortex_exc_l4,
self.parameters.sheets.l4_cortex_exc.AfferentConnection,
'V1AffConnection')
GaborConnector(self, self.input_layer.sheets['X_ON'],
self.input_layer.sheets['X_OFF'], cortex_inh_l4,
self.parameters.sheets.l4_cortex_inh.AfferentConnection,
'V1AffInhConnection')
# initialize lateral layer 4 projections
if not self.parameters.only_afferent:
ModularSamplingProbabilisticConnectorAnnotationSamplesCount(
self, 'V1L4ExcL4ExcConnection', cortex_exc_l4, cortex_exc_l4,
self.parameters.sheets.l4_cortex_exc.L4ExcL4ExcConnection
).connect()
ModularSamplingProbabilisticConnectorAnnotationSamplesCount(
self, 'V1L4ExcL4InhConnection', cortex_exc_l4, cortex_inh_l4,
self.parameters.sheets.l4_cortex_exc.L4ExcL4InhConnection
).connect()
ModularSamplingProbabilisticConnector(
self, 'V1L4InhL4ExcConnection', cortex_inh_l4, cortex_exc_l4,
self.parameters.sheets.l4_cortex_inh.L4InhL4ExcConnection
).connect()
ModularSamplingProbabilisticConnector(
self, 'V1L4InhL4InhConnection', cortex_inh_l4, cortex_inh_l4,
self.parameters.sheets.l4_cortex_inh.L4InhL4InhConnection
).connect()
if self.parameters.l23:
# if False:
# initialize afferent layer 4 to layer 2/3 projection
ModularSamplingProbabilisticConnector(
self, 'V1L4ExcL23ExcConnection', cortex_exc_l4,
cortex_exc_l23, self.parameters.sheets.l23_cortex_exc.
L4ExcL23ExcConnection).connect()
ModularSamplingProbabilisticConnector(
self, 'V1L4ExcL23InhConnection', cortex_exc_l4,
cortex_inh_l23, self.parameters.sheets.l23_cortex_inh.
L4ExcL23InhConnection).connect()
ModularSamplingProbabilisticConnector(
self, 'V1L23ExcL23ExcConnection', cortex_exc_l23,
cortex_exc_l23, self.parameters.sheets.l23_cortex_exc.
L23ExcL23ExcConnection).connect()
ModularSamplingProbabilisticConnector(
self, 'V1L23ExcL23InhConnection', cortex_exc_l23,
cortex_inh_l23, self.parameters.sheets.l23_cortex_exc.
L23ExcL23InhConnection).connect()
ModularSamplingProbabilisticConnector(
self, 'V1L23InhL23ExcConnection', cortex_inh_l23,
cortex_exc_l23, self.parameters.sheets.l23_cortex_inh.
L23InhL23ExcConnection).connect()
ModularSamplingProbabilisticConnector(
self, 'V1L23InhL23InhConnection', cortex_inh_l23,
cortex_inh_l23, self.parameters.sheets.l23_cortex_inh.
L23InhL23InhConnection).connect()
if self.parameters.feedback:
ModularSamplingProbabilisticConnector(
self, 'V1L23ExcL4ExcConnection', cortex_exc_l23,
cortex_exc_l4, self.parameters.sheets.l23_cortex_exc.
L23ExcL4ExcConnection).connect()
ModularSamplingProbabilisticConnector(
self, 'V1L23ExcL4InhConnection', cortex_exc_l23,
cortex_inh_l4, self.parameters.sheets.l23_cortex_exc.
L23ExcL4InhConnection).connect()
def __init__(self, network, lgn_on, lgn_off, target, parameters, name):
from numpy import random
random.seed(1023)
BaseComponent.__init__(self, network, parameters)
self.name = name
t_size = target.size_in_degrees()
or_map = None
if self.parameters.or_map:
f = open(self.parameters.or_map_location, 'r')
or_map = pickle.load(f)*numpy.pi
coords_x = numpy.linspace(-t_size[0]/2.0,
t_size[0]/2.0,
numpy.shape(or_map)[0])
coords_y = numpy.linspace(-t_size[1]/2.0,
t_size[1]/2.0,
numpy.shape(or_map)[1])
X, Y = numpy.meshgrid(coords_x, coords_y)
or_map = NearestNDInterpolator(zip(X.flatten(), Y.flatten()),
or_map.flatten())
phase_map = None
if self.parameters.phase_map:
f = open(self.parameters.phase_map_location, 'r')
phase_map = pickle.load(f)
coords_x = numpy.linspace(-t_size[0]/2.0,
t_size[0]/2.0,
numpy.shape(phase_map)[0])
coords_y = numpy.linspace(-t_size[1]/2.0,
t_size[1]/2.0,
numpy.shape(phase_map)[1])
X, Y = numpy.meshgrid(coords_x, coords_y)
phase_map = NearestNDInterpolator(zip(X.flatten(), Y.flatten()),
phase_map.flatten())
for (j, neuron2) in enumerate(target.pop.all()):
if or_map:
orientation = or_map(target.pop.positions[0][j],
target.pop.positions[1][j])
else:
orientation = parameters.orientation_preference.next()[0]
if phase_map:
phase = phase_map(target.pop.positions[0][j],
target.pop.positions[1][j])
else:
phase = parameters.phase.next()[0]
aspect_ratio = parameters.aspect_ratio.next()[0]
frequency = parameters.frequency.next()[0]
size = parameters.size.next()[0]
assert orientation < numpy.pi
target.add_neuron_annotation(j, 'LGNAfferentOrientation', orientation, protected=True)
target.add_neuron_annotation(j, 'LGNAfferentAspectRatio', aspect_ratio, protected=True)
target.add_neuron_annotation(j, 'LGNAfferentFrequency', frequency, protected=True)
target.add_neuron_annotation(j, 'LGNAfferentSize', size, protected=True)
target.add_neuron_annotation(j, 'LGNAfferentPhase', phase, protected=True)
if self.parameters.topological:
target.add_neuron_annotation(j, 'LGNAfferentX', target.pop.positions[0][j], protected=True)
target.add_neuron_annotation(j, 'LGNAfferentY', target.pop.positions[1][j], protected=True)
else:
target.add_neuron_annotation(j, 'LGNAfferentX', 0, protected=True)
target.add_neuron_annotation(j, 'LGNAfferentY', 0, protected=True)
ps = ParameterSet({ 'target_synapses' : 'excitatory',
'weight_functions' : { 'f1' : {
'component' : 'mozaik.connectors.vision.GaborArborization',
'params' : {
'ON' : True,
}
}
},
'delay_functions' : {},
'weight_expression' : 'f1', # a python expression that can use variables f1..fn where n is the number of functions in weight_functions, and fi corresponds to the name given to a ModularConnectorFunction in weight_function ParameterSet. It determines how are the weight functions combined to obtain the weights
'delay_expression' : str(self.parameters.delay),
'short_term_plasticity' : self.parameters.short_term_plasticity,
'base_weight' : self.parameters.base_weight,
'num_samples' : self.parameters.num_samples,
})
ModularSamplingProbabilisticConnector(network,name+'On',lgn_on,target,ps).connect()
ps['weight_functions.f1.params.ON']=False
ModularSamplingProbabilisticConnector(network,name+'Off',lgn_off,target,ps).connect()