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)
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)
# 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()
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
#or_map = pickle.load(f)*numpy.pi*2
#or_map = numpy.cos(or_map) + 1j*numpy.sin(or_map)
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())
#or_map = CloughTocher2DInterpolator(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])
#orientation = (numpy.angle(or_map(target.pop.positions[0][j],
# target.pop.positions[1][j]))+numpy.pi)/2.0
else:
orientation = parameters.orientation_preference.next()
if phase_map:
phase = phase_map(target.pop.positions[0][j],
target.pop.positions[1][j])
else:
phase = parameters.phase.next()
aspect_ratio = parameters.aspect_ratio.next()
frequency = parameters.frequency.next()
size = parameters.size.next()
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)
target.add_neuron_annotation(j,
'aff_samples',
self.parameters.num_samples.next(),
protected=True)
if self.parameters.topological:
target.add_neuron_annotation(j,
'LGNAfferentX',
target.pop.positions[0][j] +
parameters.rf_jitter.next(),
protected=True)
target.add_neuron_annotation(j,
'LGNAfferentY',
target.pop.positions[1][j] +
parameters.rf_jitter.next(),
protected=True)
else:
target.add_neuron_annotation(j,
'LGNAfferentX',
parameters.rf_jitter.next(),
protected=True)
target.add_neuron_annotation(j,
'LGNAfferentY',
parameters.rf_jitter.next(),
protected=True)
ps = ParameterSet({
'target_synapses': 'excitatory',
'weight_functions': {
'f1': {
'component': 'mozaik.connectors.vision.GaborArborization',
'params': {
'ON': True,
}
}
},
'delay_functions': self.parameters.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': self.parameters.delay_expression,
'short_term_plasticity': self.parameters.short_term_plasticity,
'base_weight': self.parameters.base_weight,
'num_samples': 0,
'annotation_reference_name': 'aff_samples',
})
ModularSamplingProbabilisticConnectorAnnotationSamplesCount(
network, name + 'On', lgn_on, target, ps).connect()
ps['weight_functions.f1.params.ON'] = False
ps['base_weight'] = self.parameters.base_weight * self.parameters.off_bias
ModularSamplingProbabilisticConnectorAnnotationSamplesCount(
network, name + 'Off', lgn_off, target, ps).connect()