预测表征：智能的基石

IF 2.7 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Computation Pub Date : 2024-10-11 DOI:10.1162/neco_a_01705

Wilka Carvalho;Momchil S. Tomov;William de Cothi;Caswell Barry;Samuel J. Gershman

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引用次数: 0

摘要

适应行为往往需要预测未来事件。强化学习理论规定了哪些类型的预测表征是有用的，以及如何计算它们。这篇综述将这些理论观点与认知和神经科学方面的工作相结合。我们特别关注继任表征及其泛化，它们已被广泛应用为工程工具和大脑功能模型。这种趋同性表明，特定类型的预测表征可以作为智能的多功能构件发挥作用。

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Predictive Representations: Building Blocks of Intelligence

Adaptive behavior often requires predicting future events. The theory of reinforcement learning prescribes what kinds of predictive representations are useful and how to compute them. This review integrates these theoretical ideas with work on cognition and neuroscience. We pay special attention to the successor representation and its generalizations, which have been widely applied as both engineering tools and models of brain function. This convergence suggests that particular kinds of predictive representations may function as versatile building blocks of intelligence.

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来源期刊

Neural Computation 工程技术-计算机：人工智能

CiteScore

6.30

自引率

3.40%

发文量

审稿时长

3.0 months

期刊介绍： Neural Computation is uniquely positioned at the crossroads between neuroscience and TMCS and welcomes the submission of original papers from all areas of TMCS, including: Advanced experimental design; Analysis of chemical sensor data; Connectomic reconstructions; Analysis of multielectrode and optical recordings; Genetic data for cell identity; Analysis of behavioral data; Multiscale models; Analysis of molecular mechanisms; Neuroinformatics; Analysis of brain imaging data; Neuromorphic engineering; Principles of neural coding, computation, circuit dynamics, and plasticity; Theories of brain function.