THESIS
2016
xi, 49 pages : illustrations ; 30 cm
Abstract
The responses of complex cells in the mammalian visual system are often modeled by the
disparity energy model. The model linearly combines inputs from binocular simple cells, whose
responses are computed by the combination of left and right eye inputs through linear receptive
fields, followed by half squaring. However, many cells’ responses cannot be explained by this
model. While it has been extended to explain some specific types of tuning, actual neurons
display richer characteristics than can be fully accounted for by these models.
Here, we describe a two layer simple cell model that can be used to construct complex
cells that fully cover this range. The model combines the responses from a first layer of
monocular simple cells. By adjusting the weights from the monocula...[
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The responses of complex cells in the mammalian visual system are often modeled by the
disparity energy model. The model linearly combines inputs from binocular simple cells, whose
responses are computed by the combination of left and right eye inputs through linear receptive
fields, followed by half squaring. However, many cells’ responses cannot be explained by this
model. While it has been extended to explain some specific types of tuning, actual neurons
display richer characteristics than can be fully accounted for by these models.
Here, we describe a two layer simple cell model that can be used to construct complex
cells that fully cover this range. The model combines the responses from a first layer of
monocular simple cells. By adjusting the weights from the monocular to a second binocular
layer, the model can exhibit more diverse tuning properties than previous models. We show
that these weights can be learned by sparse coding, and that if so, there is a strong relationship
between distribution of tuning properties in the population and the input disparity statistics.
Our simulation results also show that the modeled population evolves towards the data
recorded from cat Area 18, and we can find the optimal input disparity distribution resulting in
the most similar population with the recorded data. By changing the wavelength of model
receptive field, we demonstrate that the standard deviation of the optimal input disparity
distribution increases with the wavelength.
Keywords- stereo disparity; disparity energy model; neural population; sparse coding;
ocular dominance; disparity sensitivity
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