Orientation selectivity properties for the affine Gaussian derivative and the affine Gabor models for visual receptive fields.

IF 2 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Journal of Computational Neuroscience Pub Date : 2025-03-01 Epub Date: 2025-01-29 DOI:10.1007/s10827-024-00888-w
Tony Lindeberg
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引用次数: 0

Abstract

This paper presents an in-depth theoretical analysis of the orientation selectivity properties of simple cells and complex cells, that can be well modelled by the generalized Gaussian derivative model for visual receptive fields, with the purely spatial component of the receptive fields determined by oriented affine Gaussian derivatives for different orders of spatial differentiation. A detailed mathematical analysis is presented for the three different cases of either: (i) purely spatial receptive fields, (ii) space-time separable spatio-temporal receptive fields and (iii) velocity-adapted spatio-temporal receptive fields. Closed-form theoretical expressions for the orientation selectivity curves for idealized models of simple and complex cells are derived for all these main cases, and it is shown that the orientation selectivity of the receptive fields becomes more narrow, as a scale parameter ratio κ , defined as the ratio between the scale parameters in the directions perpendicular to vs. parallel with the preferred orientation of the receptive field, increases. It is also shown that the orientation selectivity becomes more narrow with increasing order of spatial differentiation in the underlying affine Gaussian derivative operators over the spatial domain. A corresponding theoretical orientation selectivity analysis is also presented for purely spatial receptive fields according to an affine Gabor model, showing that: (i) the orientation selectivity becomes more narrow when making the receptive fields wider in the direction perpendicular to the preferred orientation of the receptive field; while (ii) an additional degree of freedom in the affine Gabor model does, however, also strongly affect the orientation selectivity properties.

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视感受野的仿射高斯导数和仿射Gabor模型的取向选择性。
本文对简单细胞和复杂细胞的定向选择性特性进行了深入的理论分析,这种特性可以用视觉感受野的广义高斯导数模型很好地建模,感受野的纯空间分量由不同空间分异阶的定向仿射高斯导数决定。本文对三种不同的情况进行了详细的数学分析:(i)纯空间感受野,(ii)时空可分离的时空感受野和(iii)速度适应的时空感受野。在所有这些主要情况下,推导了简单和复杂细胞理想模型的取向选择性曲线的封闭形式理论表达式,并表明,随着尺度参数比κ(定义为垂直与平行于接受野首选取向方向的尺度参数之比)的增加,感受野的取向选择性变得更加狭窄。在空间域上,随着底层仿射高斯导数算子空间分异阶数的增加,取向选择性变得更窄。根据仿射Gabor模型,对纯空间感受野的取向选择性进行了理论分析,结果表明:(1)在垂直于感受野首选取向的方向上,当感受野变宽时,取向选择性变得更窄;而(ii)仿射Gabor模型中的额外自由度也会强烈影响取向选择性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.00
自引率
8.30%
发文量
32
审稿时长
3 months
期刊介绍: The Journal of Computational Neuroscience provides a forum for papers that fit the interface between computational and experimental work in the neurosciences. The Journal of Computational Neuroscience publishes full length original papers, rapid communications and review articles describing theoretical and experimental work relevant to computations in the brain and nervous system. Papers that combine theoretical and experimental work are especially encouraged. Primarily theoretical papers should deal with issues of obvious relevance to biological nervous systems. Experimental papers should have implications for the computational function of the nervous system, and may report results using any of a variety of approaches including anatomy, electrophysiology, biophysics, imaging, and molecular biology. Papers investigating the physiological mechanisms underlying pathologies of the nervous system, or papers that report novel technologies of interest to researchers in computational neuroscience, including advances in neural data analysis methods yielding insights into the function of the nervous system, are also welcomed (in this case, methodological papers should include an application of the new method, exemplifying the insights that it yields).It is anticipated that all levels of analysis from cognitive to cellular will be represented in the Journal of Computational Neuroscience.
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