A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model |
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Authors: | Email author" target="_blank">George?AzzopardiEmail author Nicolai?Petkov |
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Institution: | Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, Groningen, The Netherlands. g.azzopardi@rug.nl |
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Abstract: | Simple cells in primary visual cortex are believed to extract local contour information from a visual scene. The 2D Gabor
function (GF) model has gained particular popularity as a computational model of a simple cell. However, it short-cuts the
LGN, it cannot reproduce a number of properties of real simple cells, and its effectiveness in contour detection tasks has
never been compared with the effectiveness of alternative models. We propose a computational model that uses as afferent inputs
the responses of model LGN cells with center–surround receptive fields (RFs) and we refer to it as a Combination of Receptive
Fields (CORF) model. We use shifted gratings as test stimuli and simulated reverse correlation to explore the nature of the
proposed model. We study its behavior regarding the effect of contrast on its response and orientation bandwidth as well as
the effect of an orthogonal mask on the response to an optimally oriented stimulus. We also evaluate and compare the performances
of the CORF and GF models regarding contour detection, using two public data sets of images of natural scenes with associated
contour ground truths. The RF map of the proposed CORF model, determined with simulated reverse correlation, can be divided
in elongated excitatory and inhibitory regions typical of simple cells. The modulated response to shifted gratings that this
model shows is also characteristic of a simple cell. Furthermore, the CORF model exhibits cross orientation suppression, contrast
invariant orientation tuning and response saturation. These properties are observed in real simple cells, but are not possessed
by the GF model. The proposed CORF model outperforms the GF model in contour detection with high statistical confidence (RuG
data set: p < 10−4, and Berkeley data set: p < 10−4). The proposed CORF model is more realistic than the GF model and is more effective in contour detection, which is assumed
to be the primary biological role of simple cells. |
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