空间地图:制约约束问题的有利选择

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Zhixian Han, Anne B. Sereno
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引用次数: 0

摘要

许多研究表明,人类视觉系统有两条功能不同的主要皮层视觉通路:一条是腹侧通路,被认为对物体识别很重要;另一条是背侧通路,被认为对空间认知很重要。根据我们和其他学者之前的研究,具有两条分离通路的人工神经网络能比单通路人工神经网络更准确、更高效地确定物体的身份和位置。此外,我们还发现,这两条分离的人工皮层视觉通路可以各自独立地、以不同的方式处理视觉对象的身份和空间信息。然而,当使用这种网络处理多个物体的身份和位置时,会出现一个绑定问题,因为网络可能无法正确地将每个物体的身份与其位置联系起来。在之前的研究中,我们通过训练人工身份路径来保留物体的相对位置信息,从而限制了绑定问题。这种设计使用位置图来限制绑定问题。该研究的一个局限是,我们只考虑了物体的两个属性(身份和位置),而且只有一个可能的地图(位置)用于绑定。然而,大脑通常需要处理和绑定对象的多个属性,这些属性中的任何一个都可以用来限制绑定问题。在我们目前的研究中,我们利用需要识别的具有多种属性(身份、亮度、方向和位置)的视觉对象,试图找到约束绑定问题的最佳映射(身份映射、亮度映射、方向映射或位置映射)。我们发现,在我们的实验模拟中,当视觉属性相互独立时,位置图总是比其他类型的地图更适合用来限制绑定问题。我们的研究结果与之前的神经生理学研究结果一致,这些研究结果表明,许多视觉皮层区域的组织或地图主要是视网膜定位或空间定位的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A spatial map: a propitious choice for constraining the binding problem
Many studies have shown that the human visual system has two major functionally distinct cortical visual pathways: a ventral pathway, thought to be important for object recognition, and a dorsal pathway, thought to be important for spatial cognition. According to our and others previous studies, artificial neural networks with two segregated pathways can determine objects' identities and locations more accurately and efficiently than one-pathway artificial neural networks. In addition, we showed that these two segregated artificial cortical visual pathways can each process identity and spatial information of visual objects independently and differently. However, when using such networks to process multiple objects' identities and locations, a binding problem arises because the networks may not associate each object's identity with its location correctly. In a previous study, we constrained the binding problem by training the artificial identity pathway to retain relative location information of objects. This design uses a location map to constrain the binding problem. One limitation of that study was that we only considered two attributes of our objects (identity and location) and only one possible map (location) for binding. However, typically the brain needs to process and bind many attributes of an object, and any of these attributes could be used to constrain the binding problem. In our current study, using visual objects with multiple attributes (identity, luminance, orientation, and location) that need to be recognized, we tried to find the best map (among an identity map, a luminance map, an orientation map, or a location map) to constrain the binding problem. We found that in our experimental simulations, when visual attributes are independent of each other, a location map is always a better choice than the other kinds of maps examined for constraining the binding problem. Our findings agree with previous neurophysiological findings that show that the organization or map in many visual cortical areas is primarily retinotopic or spatial.
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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