Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/8638
Title: A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model
Authors: Azzopardi, George
Petkov, Nicolai
Keywords: Computer simulation
Gabor transforms
G-protein signalling modulator 2 protein, human
Issue Date: 2012
Publisher: Springer
Citation: Biological Cybernetics. 2012, Vol.106(3), p. 177-189, 2012
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 popu- larity as a computational model of a simple cell. However, it short-cuts the LGN, it cannot reproduce a number of proper- ties of real simple cells, and its effectiveness in contour detec- tion 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 corre- lation 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 orthog- onal mask on the response to an optimally oriented stimu- lus. 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 sup- pression, 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 con- tour detection, which is assumed to be the primary biological role of simple cells.
URI: https://www.um.edu.mt/library/oar//handle/123456789/8638
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