# Retinotopy: Predicting the perceptual effects of different visual field maps¶

Every computational model needs to assume a mapping between retinal and visual field coordinates. A number of these visual field maps are provided in the geometry module of the utilities subpackage:

All of these visual field maps follow the VisualFieldMap template. This means that they have to specify a dva2ret method, which transforms visual field coordinates into retinal coordinates, and a complementary ret2dva method.

## Visual field maps¶

To appreciate the difference between the available visual field maps, let us look at a rectangular grid in visual field coordinates:

import pulse2percept as p2p
import matplotlib.pyplot as plt

grid = p2p.utils.Grid2D((-50, 50), (-50, 50), step=5)
grid.plot(style='scatter')
plt.xlabel('x (degrees of visual angle)')
plt.ylabel('y (degrees of visual angle)')
plt.axis('square')


Out:

(-55.0, 55.0, -55.0, 55.0)


Such a grid is typically created during a model’s build process and defines at which (x,y) locations the percept is to be evaluated.

However, these visual field coordinates are mapped onto different retinal coordinates under the three visual field maps:

transforms = [p2p.utils.Curcio1990Map,
p2p.utils.Watson2014Map,
p2p.utils.Watson2014DisplaceMap]
fig, axes = plt.subplots(ncols=3, sharey=True, figsize=(13, 4))
for ax, transform in zip(axes, transforms):
grid.plot(transform=transform().dva2ret, style='cell', ax=ax)
ax.set_title(transform().__class__.__name__)
ax.set_xlabel('x (microns)')
ax.set_ylabel('y (microns)')
ax.axis('equal')


Whereas the [Curcio1990] map applies a simple scaling factor to the visual field coordinates, [Watson2014] uses a nonlinear transform. One thing to note is the RGC displacement zone in the third panel, which might lead to distortions in the fovea.

## Perceptual distortions¶

The perceptual consequences of these visual field maps become apparent when used in combination with an implant.

For this purpose, let us create an AlphaAMS device on the fovea and feed it a suitable stimulus:

implant = p2p.implants.AlphaAMS(stim=p2p.stimuli.LogoUCSB())
implant.stim


Out:

/home/docs/checkouts/readthedocs.org/user_builds/pulse2percept/envs/latest/lib/python3.7/site-packages/pulse2percept/stimuli/images.py:201: FutureWarning: The behavior of rgb2gray will change in scikit-image 0.19. Currently, rgb2gray allows 2D grayscale image to be passed as inputs and leaves them unmodified as outputs. Starting from version 0.19, 2D arrays will be treated as 1D images with 3 channels.
return ImageStimulus(rgb2gray(img), electrodes=electrodes,

Stimulus(data=[[0.], [0.], [0.], ..., [0.], [0.], [0.]],
dt=0.001,
electrodes=['A1' 'A2' 'A3' ... 'AN38' 'AN39' 'AN40'],
shape=(1600, 1), time=None)


We can easily switch out the visual field maps by passing a retinotopy attribute to ScoreboardModel (by default, the scoreboard model will use [Curcio1990]):

fig, axes = plt.subplots(ncols=3, sharey=True, figsize=(13, 4))
for ax, transform in zip(axes, transforms):
model = p2p.models.ScoreboardModel(xrange=(-6, 6), yrange=(-6, 6),
retinotopy=transform())
model.build()
model.predict_percept(implant).plot(ax=ax)
ax.set_title(transform().__class__.__name__)


Whereas the left and center panel look virtually identical, the rightmost panel predicts a rather striking perceptual effect of the RGC displacement zone.

## Creating your own visual field map¶

To create your own visual field map, you need to subclass the VisualFieldMap template and provide your own dva2ret and ret2dva methods. For example, the following class would (wrongly) assume that retinal coordinates are identical to visual field coordinates:

class MyVisualFieldMap(p2p.utils.VisualFieldMap):

def dva2ret(self, xdva, ydva):
return xdva, ydva

def ret2dva(self, xret, yret):
return xret, yret


To use it with a model, you need to pass retinotopy=MyVisualFieldMap() to the model’s constructor.

Total running time of the script: ( 0 minutes 2.158 seconds)

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