A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model

被引:120
作者
Azzopardi, George [1 ]
Petkov, Nicolai [1 ]
机构
[1] Univ Groningen, Johann Bernoulli Inst Math & Comp Sci, Groningen, Netherlands
关键词
Aligned receptive fields; Computational model; Contour detection; Gabor function; LGN; Simple cell; CAT VISUAL-CORTEX; LATERAL GENICULATE-NUCLEUS; RECEPTIVE-FIELDS; STRIATE CORTEX; SPATIAL-FREQUENCY; EDGE-DETECTION; ORIENTATION SELECTIVITY; CORTICAL-CELLS; THALAMIC INPUT; RESPONSES;
D O I
10.1007/s00422-012-0486-6
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
080201 [机械制造及其自动化];
摘要
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.
引用
收藏
页码:177 / 189
页数:13
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