Active Predictive Coding: A Unifying Neural Model for Active Perception, Compositional Learning, and Hierarchical Planning

IF 2.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Rajesh P. N. Rao;Dimitrios C. Gklezakos;Vishwas Sathish
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引用次数: 0

Abstract

There is growing interest in predictive coding as a model of how the brain learns through predictions and prediction errors. Predictive coding models have traditionally focused on sensory coding and perception. Here we introduce active predictive coding (APC) as a unifying model for perception, action, and cognition. The APC model addresses important open problems in cognitive science and AI, including (1) how we learn compositional representations (e.g., part-whole hierarchies for equivariant vision) and (2) how we solve large-scale planning problems, which are hard for traditional reinforcement learning, by composing complex state dynamics and abstract actions from simpler dynamics and primitive actions. By using hypernetworks, self-supervised learning, and reinforcement learning, APC learns hierarchical world models by combining task-invariant state transition networks and task-dependent policy networks at multiple abstraction levels. We illustrate the applicability of the APC model to active visual perception and hierarchical planning. Our results represent, to our knowledge, the first proof-of-concept demonstration of a unified approach to addressing the part-whole learning problem in vision, the nested reference frames learning problem in cognition, and the integrated state-action hierarchy learning problem in reinforcement learning.
主动预测编码:主动感知、组合学习和分层规划的统一神经模型
预测编码作为大脑如何通过预测和预测错误进行学习的模型,越来越受到人们的关注。预测编码模型历来侧重于感官编码和感知。在这里,我们将主动预测编码(APC)作为感知、行动和认知的统一模型加以介绍。主动预测编码模型解决了认知科学和人工智能领域的重要开放性问题,包括:(1)我们如何学习组合表征(如等变视觉的部分-整体层次结构);(2)我们如何通过将复杂的状态动态和抽象动作与较简单的动态和原始动作组合起来,解决传统强化学习难以解决的大规模规划问题。通过使用超网络、自监督学习和强化学习,APC 在多个抽象层级上结合任务不变状态转换网络和任务相关策略网络,学习分层世界模型。我们说明了 APC 模型在主动视觉感知和分层规划方面的适用性。据我们所知,我们的研究结果代表了首次概念验证,展示了一种统一的方法来解决视觉中的部分-整体学习问题、认知中的嵌套参照系学习问题以及强化学习中的综合状态-行动层次学习问题。
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来源期刊
Neural Computation
Neural Computation 工程技术-计算机:人工智能
CiteScore
6.30
自引率
3.40%
发文量
83
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.
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