具有频率和相位的视觉皮层亚黎曼模型。

IF 2.3 4区 医学 Q1 Neuroscience
E Baspinar, A Sarti, G Citti
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引用次数: 10

摘要

本文基于初级视觉皮层简单细胞的定向、频率和相位选择行为,提出了一种新的初级视觉皮层(V1)模型。我们从视觉感知的第一级机制,接受性轮廓开始。该模型通过引入方向、频率和相位作为固有变量,将V1解释为二维视网膜平面上的纤维束。光纤上的每个接收剖面在数学上被解释为旋转、调频和相移的Gabor函数。我们从Gabor函数开始,并证明它以自然的方式诱导了V1中神经连接模式的模型几何和相关的水平连接建模。我们提出了一种基于模型框架的图像增强算法。该算法不仅可以利用二维输入图像中固有的方向信息,还可以利用其固有的频率和相位信息。给出了与增强算法相对应的实验结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A sub-Riemannian model of the visual cortex with frequency and phase.

A sub-Riemannian model of the visual cortex with frequency and phase.

A sub-Riemannian model of the visual cortex with frequency and phase.

A sub-Riemannian model of the visual cortex with frequency and phase.

In this paper, we present a novel model of the primary visual cortex (V1) based on orientation, frequency, and phase selective behavior of V1 simple cells. We start from the first-level mechanisms of visual perception, receptive profiles. The model interprets V1 as a fiber bundle over the two-dimensional retinal plane by introducing orientation, frequency, and phase as intrinsic variables. Each receptive profile on the fiber is mathematically interpreted as rotated, frequency modulated, and phase shifted Gabor function. We start from the Gabor function and show that it induces in a natural way the model geometry and the associated horizontal connectivity modeling of the neural connectivity patterns in V1. We provide an image enhancement algorithm employing the model framework. The algorithm is capable of exploiting not only orientation but also frequency and phase information existing intrinsically in a two-dimensional input image. We provide the experimental results corresponding to the enhancement algorithm.

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来源期刊
Journal of Mathematical Neuroscience
Journal of Mathematical Neuroscience Neuroscience-Neuroscience (miscellaneous)
自引率
0.00%
发文量
0
审稿时长
13 weeks
期刊介绍: The Journal of Mathematical Neuroscience (JMN) publishes research articles on the mathematical modeling and analysis of all areas of neuroscience, i.e., the study of the nervous system and its dysfunctions. The focus is on using mathematics as the primary tool for elucidating the fundamental mechanisms responsible for experimentally observed behaviours in neuroscience at all relevant scales, from the molecular world to that of cognition. The aim is to publish work that uses advanced mathematical techniques to illuminate these questions. It publishes full length original papers, rapid communications and review articles. Papers that combine theoretical results supported by convincing numerical experiments are especially encouraged. Papers that introduce and help develop those new pieces of mathematical theory which are likely to be relevant to future studies of the nervous system in general and the human brain in particular are also welcome.
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