探索灵长类动物物体识别的综合神经机制。

IF 5 2区 医学 Q1 NEUROSCIENCES
Kohitij Kar, James J DiCarlo
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引用次数: 0

摘要

尽管不同的视角和场景给算法带来了挑战,但通过视觉对物体进行推断(如快速而准确的分类)是灵长类动物认知的核心。直到最近,支持这些能力的大脑机制仍深藏不露。然而,在过去的十年中,这一科学之谜已被受大脑启发的、可进行图像计算的人工神经网络(ANN)系统的发现和发展所揭开,这些系统在这些行为功能方面可与灵长类动物媲美。除了从根本上改变了人工智能的面貌之外,这些人工神经网络系统的改进版也是目前科学界对灵长类动物腹侧视觉流中支持核心物体识别的一套综合机制的主要假设。这些系统的脑图版本与之前的概念模型的不同之处在于,它们具有感官可计算性、机械性、解剖参考性和可测试性(SMART)。在本文中,我们将对当前领先的 SMART 模型所涉及的大脑机制进行回顾和透视。我们回顾了这些模型在大脑和行为配准方面的成功和失败经验,讨论了更准确的机理理解的下一个前沿领域,并概述了可能的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Quest for an Integrated Set of Neural Mechanisms Underlying Object Recognition in Primates.

Inferences made about objects via vision, such as rapid and accurate categorization, are core to primate cognition despite the algorithmic challenge posed by varying viewpoints and scenes. Until recently, the brain mechanisms that support these capabilities were deeply mysterious. However, over the past decade, this scientific mystery has been illuminated by the discovery and development of brain-inspired, image-computable, artificial neural network (ANN) systems that rival primates in these behavioral feats. Apart from fundamentally changing the landscape of artificial intelligence, modified versions of these ANN systems are the current leading scientific hypotheses of an integrated set of mechanisms in the primate ventral visual stream that support core object recognition. What separates brain-mapped versions of these systems from prior conceptual models is that they are sensory computable, mechanistic, anatomically referenced, and testable (SMART). In this article, we review and provide perspective on the brain mechanisms addressed by the current leading SMART models. We review their empirical brain and behavioral alignment successes and failures, discuss the next frontiers for an even more accurate mechanistic understanding, and outline the likely applications.

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来源期刊
Annual Review of Vision Science
Annual Review of Vision Science Medicine-Ophthalmology
CiteScore
11.10
自引率
1.70%
发文量
19
期刊介绍: The Annual Review of Vision Science reviews progress in the visual sciences, a cross-cutting set of disciplines which intersect psychology, neuroscience, computer science, cell biology and genetics, and clinical medicine. The journal covers a broad range of topics and techniques, including optics, retina, central visual processing, visual perception, eye movements, visual development, vision models, computer vision, and the mechanisms of visual disease, dysfunction, and sight restoration. The study of vision is central to progress in many areas of science, and this new journal will explore and expose the connections that link it to biology, behavior, computation, engineering, and medicine.
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