def afferent(self, src_properties, dest_properties):
channel = dest_properties['SF'] if 'SF' in dest_properties else 1
centerg = imagen.Gaussian(
size=0.07385 * self.sf_spacing**(channel - 1),
aspect_ratio=1.0,
output_fns=[transferfn.DivisiveNormalizeL1()])
surroundg = imagen.Gaussian(
size=(4 * 0.07385) * self.sf_spacing**(channel - 1),
aspect_ratio=1.0,
output_fns=[transferfn.DivisiveNormalizeL1()])
on_weights = imagen.Composite(generators=[centerg, surroundg],
operator=numpy.subtract)
off_weights = imagen.Composite(generators=[surroundg, centerg],
operator=numpy.subtract)
#TODO: strength=+strength_scale/len(cone_types) for 'On' center
#TODO: strength=-strength_scale/len(cone_types) for 'Off' center
#TODO: strength=-strength_scale/len(cone_types) for 'On' surround
#TODO: strength=+strength_scale/len(cone_types) for 'Off' surround
return Model.SharedWeightCFProjection.params(
delay=0.05,
strength=2.33 * self.strength_factor,
name='Afferent',
nominal_bounds_template=sheet.BoundingBox(
radius=self.lgnaff_radius * self.sf_spacing**(channel - 1)),
weights_generator=on_weights
if dest_properties['polarity'] == 'On' else off_weights)
def lateral_gain_control(self, src_properties, dest_properties):
#TODO: Are those 0.25 the same as lgnlateral_radius/2.0?
return Model.SharedWeightCFProjection.params(
delay=0.05,
dest_port=('Activity'),
activity_group=(0.6, DivideWithConstant(c=0.11)),
weights_generator=imagen.Gaussian(
size=0.25,
aspect_ratio=1.0,
output_fns=[transferfn.DivisiveNormalizeL1()]),
nominal_bounds_template=sheet.BoundingBox(radius=0.25),
name=('LateralGC' + src_properties['eye']
if 'eye' in src_properties else 'LateralGC'),
strength=0.6 / len(self.attrs.Eyes))
def afferent_surround(self, src_properties, dest_properties):
surrounds=[]
for color, color_code in self.ColorToChannel.items():
if color in dest_properties['surround']:
surrounds.append(color_code)
return [Model.SharedWeightCFProjection.params(
delay=0.05,
strength=(4.7/2.33)*self.lgnaff_strength/len(surrounds)*(-1 if dest_properties['polarity'] == 'On' else 1),
src_port='Activity%d'%surround,
dest_port='Activity',
weights_generator=imagen.Gaussian(size=0.07385*(4 if dest_properties['opponent'] is not 'Blue' else 1),
aspect_ratio=1.0,
output_fns=[transferfn.DivisiveNormalizeL1()]),
name='AfferentSurround'+str(surround),
nominal_bounds_template=sheet.BoundingBox(radius=self.lgnaff_radius))
for surround in surrounds]
def afferent_surround(self, src_properties, dest_properties):
#TODO: strength=-strength_scale for 'On', +strength_scale for 'Off'
#TODO: strength=-strength_scale/2 for dest_properties['opponent']=='Blue'
# dest_properties['surround']=='RedGreen' and dest_properties['polarity']=='On'
#TODO: strength=+strength_scale/2 for above, but 'Off'
#TODO: strength=-strength_scale/len(cone_types) for Luminosity 'On'
#TODO: strength=+strength_scale/len(cone_types) for Luminosity 'Off'
return Model.SharedWeightCFProjection.params(
delay=0.05,
strength=2.33 * self.strength_factor,
weights_generator=imagen.Gaussian(
size=4 * 0.07385,
aspect_ratio=1.0,
output_fns=[transferfn.DivisiveNormalizeL1()]),
name='AfferentSurround' + src_properties['cone'],
nominal_bounds_template=sheet.BoundingBox(
radius=self.lgnaff_radius))
def lateral_gain_control(self, src_properties, dest_properties):
#TODO: Are those 0.25 the same as lgnlateral_radius/2.0?
name='LateralGC'
if 'eye' in src_properties:
name+=src_properties['eye']
if 'SF' in src_properties and self.gain_control_SF:
name+=('SF'+str(src_properties['SF']))
return Model.SharedWeightCFProjection.params(
delay=0.05,
dest_port=('Activity'),
activity_group=(0.6,DivideWithConstant(c=0.11)),
weights_generator=imagen.Gaussian(size=self.gain_control_size,
aspect_ratio=1.0,
output_fns=[transferfn.DivisiveNormalizeL1()]),
nominal_bounds_template=sheet.BoundingBox(radius=self.gain_control_size),
name=name,
strength=0.6/(2 if self['binocular'] else 1))
def afferent_center(self, src_properties, dest_properties):
#TODO: It shouldn't be too hard to figure out how many retina sheets it connects to,
# then all the below special cases can be generalized!
#TODO: strength=+strength_scale for 'On', strength=-strength_scale for 'Off'
#TODO: strength=+strength_scale for dest_properties['opponent']=='Blue'
# dest_properties['surround']=='RedGreen' and dest_properties['polarity']=='On'
#TODO: strength=-strength_scale for above, but 'Off'
#TODO: strength=+strength_scale/len(cone_types) for Luminosity 'On'
#TODO: strength=-strength_scale/len(cone_types) for Luminosity 'Off'
return Model.SharedWeightCFProjection.params(
delay=0.05,
strength=2.33 * self.strength_factor,
weights_generator=imagen.Gaussian(
size=0.07385,
aspect_ratio=1.0,
output_fns=[transferfn.DivisiveNormalizeL1()]),
name='AfferentCenter' + src_properties['cone'],
nominal_bounds_template=sheet.BoundingBox(
radius=self.lgnaff_radius))