Predictive coding under the free-energy principle
Predictive coding under the free-energy principle
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DOI：
10.1098/rstb.2008.0300
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发表时间：
2009-05-12
影响因子：
6.3
通讯作者：
Kiebel, Stefan
中科院分区：
文献类型：
--
作者：
Friston, Karl J.;Kiebel, Stefan
本文将预测和感知分类视为由大脑解决的推理问题。我们假设大脑将世界建模为动态系统的层次结构或级联，这些系统在感觉中编码因果结构。感知等同于这些内部模型的优化或反演，以解释感官数据。给定一个如何生成传感数据的模型，我们可以基于模型证据上的自由能约束，调用一种通用的模型反演方法。随后的自由能公式提供了描述识别过程的方程，即代表感觉输入原因的神经元活动的动力学。在这里，我们关注一个非常通用的模型，其分层和动态结构使模拟大脑能够识别和预测感觉状态的轨迹或序列。我们首先回顾层次动力学模型及其反演。然后，我们证明大脑具有实现这种反转所需的基础设施，并使用能够识别和分类鸟鸣的合成鸟类来说明这一点。
This paper considers prediction and perceptual categorization as an inference problem that is solved by the brain. We assume that the brain models the world as a hierarchy or cascade of dynamical systems that encode causal structure in the sensorium. Perception is equated with the optimization or inversion of these internal models, to explain sensory data. Given a model of how sensory data are generated, we can invoke a generic approach to model inversion, based on a free energy bound on the model's evidence. The ensuing free-energy formulation furnishes equations that prescribe the process of recognition, i.e. the dynamics of neuronal activity that represent the causes of sensory input. Here, we focus on a very general model, whose hierarchical and dynamical structure enables simulated brains to recognize and predict trajectories or sequences of sensory states. We first review hierarchical dynamical models and their inversion. We then show that the brain has the necessary infrastructure to implement this inversion and illustrate this point using synthetic birds that can recognize and categorize birdsongs.