def measure_tae():
print "Measuring initial perception of all orientations..."
before=test_all_orientations(0.0,0.0)
pylab.figure(figsize=(5,5))
vectorplot(degrees(before.keys()), degrees(before.keys()),style="--") # add a dashed reference line
vectorplot(degrees(before.values()),degrees(before.keys()),\
title="Initial perceived values for each orientation")
print "Adapting to pi/2 gaussian at the center of retina for 90 iterations..."
for p in ["LateralExcitatory","LateralInhibitory","LGNOnAfferent","LGNOffAfferent"]:
# Value is just an approximate match to bednar:nc00; not calculated directly
topo.sim["V1"].projections(p).learning_rate = 0.005
inputs = [pattern.Gaussian(x = 0.0, y = 0.0, orientation = pi/2.0,
size=0.088388, aspect_ratio=4.66667, scale=1.0)]
topo.sim['Retina'].input_generator.generators = inputs
topo.sim.run(90)
print "Measuring adapted perception of all orientations..."
after=test_all_orientations(0.0,0.0)
before_vals = array(before.values())
after_vals = array(after.values())
diff_vals = before_vals-after_vals # Sign flipped to match conventions
pylab.figure(figsize=(5,5))
pylab.axvline(90.0)
pylab.axhline(0.0)
vectorplot(wrap(-90.0,90.0,degrees(diff_vals)),degrees(before.keys()),\
title="Difference from initial perceived value for each orientation")
def measure_dae(scale=0.6):
print "Measuring initial perception of all directions..."
before=test_all_directions(0.0,0.0,scale)
pylab.figure(figsize=(5,5))
vectorplot(degrees(before.keys()), degrees(before.keys()),style="--") # add a dashed reference line
vectorplot(degrees(before.values()),degrees(before.keys()),\
title="Initial perceived values for each direction")
print "Adapting to pi/2 gaussian at the center of retina for 90 iterations..."
for p in ["LateralExcitatory","LateralInhibitory",
"LGNOnAfferent0","LGNOffAfferent0",
"LGNOnAfferent1","LGNOffAfferent1",
"LGNOnAfferent2","LGNOffAfferent2",
"LGNOnAfferent3","LGNOffAfferent3"]:
# Value is just an approximate match to bednar:nc00; not calculated directly
topo.sim["V1"].projections(p).learning_rate = 0.005
## g = pattern.Gaussian(x=0.0,y=0.0,orientation=pi/2.0,size=0.088388,
## aspect_ratio=4.66667,scale=1.0)
g = pattern.SineGrating(frequency=2.4,phase=0.0,orientation=pi/2,scale=scale)
for j in range(4):
topo.sim['Retina%s'%j].set_input_generator(pattern.Sweeper(
generator=copy.deepcopy(g),
speed=2.0/24.0,
step=j))
topo.sim.run(90)
print "Measuring adapted perception of all directions..."
after=test_all_directions(0.0,0.0,scale)
before_vals = array(before.values())
after_vals = array(after.values())
diff_vals = before_vals-after_vals # Sign flipped to match conventions
pylab.figure(figsize=(5,5))
pylab.axvline(180.0)
pylab.axhline(0.0)
vectorplot(wrap(-2*90.0,2*90.0,degrees(diff_vals)),degrees(before.keys()),\
title="Difference from initial perceived value for each direction")