def __init__(self, network, source, target, parameters,name):
MozaikComponent.__init__(self,network,parameters)
self.name = name
self.source = source
self.target = target
weights = []
for (i,neuron1) in enumerate(self.target.pop.all()):
for (j,neuron2) in enumerate(self.source.pop.all()):
or_dist = circular_dist(self.target.get_neuron_annotation(i,'LGNAfferentOrientation'),self.source.get_neuron_annotation(j,'LGNAfferentOrientation'),numpy.pi) / (numpy.pi/2)
if self.parameters.specific_arborization.target_synapses == 'excitatory':
phase_dist = circular_dist(self.target.get_neuron_annotation(i,'LGNAfferentPhase'),self.source.get_neuron_annotation(j,'LGNAfferentPhase'),2*numpy.pi) / numpy.pi
elif self.parameters.specific_arborization.target_synapses == 'inhibitory':
phase_dist = (numpy.pi - circular_dist(self.target.get_neuron_annotation(i,'LGNAfferentPhase'),self.source.get_neuron_annotation(j,'LGNAfferentPhase'),2*numpy.pi)) / numpy.pi
else:
logger.error('Unknown type of synapse!')
return
or_gauss = normal_function(or_dist,mean=0,sigma=self.parameters.or_sigma)
phase_gauss = normal_function(phase_dist,mean=0,sigma=self.parameters.or_sigma)
w = phase_gauss*or_gauss*or_gauss
weights.append((j,i,w,self.parameters.propagation_constant))
we = numpy.zeros((len(self.source.pop),len(self.target.pop)))
pnv_source = []
pnv_target = []
for (i,neuron1) in enumerate(self.target.pop.all()):
pnv_target.append(self.target.get_neuron_annotation(i,'LGNAfferentOrientation'))
for (i,neuron1) in enumerate(self.source.pop.all()):
pnv_source.append(self.source.get_neuron_annotation(i,'LGNAfferentOrientation'))
for (j,i,w,xsj) in weights:
we[j,i] = w
(angle,mag) = circ_mean(numpy.array([numpy.array(pnv_target).T for i in xrange(0,numpy.shape(we)[0])]),weights=numpy.array(we),low=0,high=numpy.pi,axis=1)
if self.parameters.probabilistic:
SpecificProbabilisticArborization(network, self.source, self.target, weights,self.parameters.specific_arborization,self.name).connect()
else:
SpecificArborization(network, self.source, self.target, weights,self.parameters.specific_arborization,self.name).connect()
def __init__(self, network, lgn_on, lgn_off, target, parameters,name):
MozaikComponent.__init__(self, network,parameters)
import pickle
self.name = name
on = lgn_on.pop
off = lgn_off.pop
on_weights=[]
off_weights=[]
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]
# HACK!!!
#if j == 0:
# orientation = 0
# if target.name=='V1_Exc':
# phase = 0
# elif target.name=='V1_Inh':
# phase = 0
aspect_ratio = parameters.aspect_ratio.next()[0]
frequency = parameters.frequency.next()[0]
size = parameters.size.next()[0]
if orientation > numpy.pi:
print orientation
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)
for (i,neuron1) in enumerate(on.all()):
if parameters.topological:
on_weights.append((i,j,numpy.max((0,gabor(on.positions[0][i],on.positions[1][i],target.pop.positions[0][j],target.pop.positions[1][j],orientation+numpy.pi/2,frequency,phase,size,aspect_ratio))),parameters.delay))
off_weights.append((i,j,-numpy.min((0,gabor(off.positions[0][i],off.positions[1][i],target.pop.positions[0][j],target.pop.positions[1][j],orientation+numpy.pi/2,frequency,phase,size,aspect_ratio))),parameters.delay))
else:
on_weights.append((i,j,numpy.max((0,gabor(on.positions[0][i],on.positions[1][i],0,0,orientation+numpy.pi/2,frequency,phase,size,aspect_ratio))),parameters.delay))
off_weights.append((i,j,-numpy.min((0,gabor(off.positions[0][i],off.positions[1][i],0,0,orientation+numpy.pi/2,frequency,phase,size,aspect_ratio))),parameters.delay))
if parameters.probabilistic:
on_proj = SpecificProbabilisticArborization(network,lgn_on,target,on_weights,parameters.specific_arborization,'ON_to_[' + target.name + ']')
off_proj = SpecificProbabilisticArborization(network,lgn_off,target,off_weights,parameters.specific_arborization,'OFF_to_[' + target.name + ']')
else:
on_proj = SpecificArborization(network,lgn_on,target,on_weights,parameters.specific_arborization,'ON_to_[' + target.name + ']')
off_proj = SpecificArborization(network,lgn_off,target,off_weights,parameters.specific_arborization,'OFF_to_[' + target.name + ']')
on_proj.connect()
off_proj.connect()