How does the brain solve visual object recognition?

How does the brain solve visual object recognition?
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DOI:
10.1016/j.neuron.2012.01.010
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发表时间:
2012-02-09
期刊:
影响因子:
16.2
通讯作者:
Rust NC
Rust NC
中科院分区:
医学1区
文献类型:
--
作者:
DiCarlo JJ;Zoccolan D;Rust NC

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越来越多的证据表明,尽管外表有很大变化,但仍能快速识别对象的能力,在大脑中是通过一系列反射性的、主要是前馈的计算在大脑中解决的,这些计算最终在颞叶下部皮质产生强大的神经元表征。然而,产生这种解决方案的算法仍然鲜为人知。在这里,我们回顾了从单个神经元到神经元群体,再到行为,再到计算模型的证据。我们认为,理解这一算法将需要使用神经元和心理物理数据来筛选许多计算模型,每个模型都基于具有共同功能目标的小型规范子网络的构建块。
Mounting evidence suggests that “core object recognition,” the ability to rapidly recognize objects despite substantial appearance variation, is solved in the brain via a cascade of reflexive, largely feedforward computations that culminate in a powerful neuronal representation in the inferior temporal cortex. However, the algorithm that produces this solution remains little-understood. Here we review evidence ranging from individual neurons, to neuronal populations, to behavior, to computational models. We propose that understanding this algorithm will require using neuronal and psychophysical data to sift through many computational models, each based on building blocks of small, canonical sub-networks with a common functional goal.
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