Cognitive maps from predictive vision

IF 18.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Nature Machine Intelligence Pub Date : 2024-08-08 DOI:10.1038/s42256-024-00885-9

Margaret C. von Ebers, Xue-Xin Wei

引用次数: 0

Abstract

Constructing spatial maps from sensory inputs is challenging in both neuroscience and artificial intelligence. A recent study demonstrates that a self-attention neural network using predictive coding can generate an environmental map in its latent space as an agent that navigates the environment.

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来自预测视觉的认知地图

从感觉输入构建空间地图在神经科学和人工智能领域都具有挑战性。最近的一项研究表明，使用预测编码的自我注意神经网络可以在其潜在空间中生成环境地图，成为一个在环境中导航的代理。

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

Nature Machine Intelligence Multiple-

CiteScore

36.90

自引率

2.10%

发文量

127

期刊介绍： Nature Machine Intelligence is a distinguished publication that presents original research and reviews on various topics in machine learning, robotics, and AI. Our focus extends beyond these fields, exploring their profound impact on other scientific disciplines, as well as societal and industrial aspects. We recognize limitless possibilities wherein machine intelligence can augment human capabilities and knowledge in domains like scientific exploration, healthcare, medical diagnostics, and the creation of safe and sustainable cities, transportation, and agriculture. Simultaneously, we acknowledge the emergence of ethical, social, and legal concerns due to the rapid pace of advancements. To foster interdisciplinary discussions on these far-reaching implications, Nature Machine Intelligence serves as a platform for dialogue facilitated through Comments, News Features, News & Views articles, and Correspondence. Our goal is to encourage a comprehensive examination of these subjects. Similar to all Nature-branded journals, Nature Machine Intelligence operates under the guidance of a team of skilled editors. We adhere to a fair and rigorous peer-review process, ensuring high standards of copy-editing and production, swift publication, and editorial independence.