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

机译:依赖LGN输入的简单单元格的CORF计算模型优于Gabor函数模型

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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.
机译:据信初级视觉皮层中的简单细胞可从视觉场景中提取局部轮廓信息。二维Gabor函数(GF)模型作为简单单元的计算模型而特别受欢迎。但是,它是LGN的捷径,它不能重现实际简单单元格的许多属性,并且从未将其在轮廓检测任务中的有效性与替代模型的有效性进行比较。我们提出了一个计算模型,该模型使用具有中心环绕接收场(RF)的模型LGN细胞的响应作为传入输入,并将其称为接收场组合(CORF)模型。我们使用位移光栅作为测试刺激并模拟反向相关性来探索所提出模型的性质。我们研究了关于其响应和定向带宽的对比度影响以及正交掩模对最佳定向刺激的响应的行为。我们还使用两个公共自然景观图像数据集和相关的轮廓地面真相,评估和比较了CORF和GF模型在轮廓检测方面的性能。拟议的CORF模型的RF图,用模拟的反向相关性确定,可以划分为简单细胞典型的拉长的兴奋性和抑制性区域。该模型显示的对位移光栅的调制响应也是简单单元的特征。此外,CORF模型表现出横向定向抑制,对比度不变的定向调整和响应饱和。这些属性是在真正的简单细胞中观察到的,但GF模型并不具备。提出的CORF模型在轮廓检测中优于GF模型,具有很高的统计置信度(RuG数据集:p <10〜(-4),Berkeley数据集:p <10〜(-4))。提出的CORF模型比GF模型更现实,并且在轮廓检测中更有效,这被认为是简单细胞的主要生物学作用。

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