Active Inference: A Process Theory
Active Inference: A Process Theory
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DOI：
10.1162/neco_a_00912
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发表时间：
2017-01-01
影响因子：
2.9
通讯作者：
Pezzulo, Giovanni
中科院分区：
文献类型：
--
作者：
Friston, Karl;FitzGerald, Thomas;Pezzulo, Giovanni
本文描述了一种基于主动推理和信念传播的过程理论。从所有的神经元处理（和动作选择）的前提下，可以通过最大化贝叶斯模型evidenceor最小化变分自由能来解释，我们问神经元的反应是否可以被描述为一个梯度下降的变分自由能。使用一个标准的（马尔可夫决策过程）生成模型，我们推导出隐含在这个描述中的神经元动力学，并再现了一个显着的范围内的特征良好的神经元现象。这些包括重复抑制，错配负性，违规反应，位置细胞活动，相位进动，θ序列，θ-γ耦合，证据积累，竞争约束动力学和多巴胺反应的转移。此外，这些动力学所规定的（近似贝叶斯最优）行为具有一定程度的表面有效性，为奖励寻求，上下文学习和认知觅食提供了正式的解释。从技术上讲，梯度下降似乎是神经元活动的有效描述，这意味着变分自由能是神经元动力学的李雅普诺夫函数，因此符合汉密尔顿的最小作用量原理。
This article describes a process theory based on active inference and belief propagation. Starting from the premise that all neuronal processing (and action selection) can be explained by maximizing Bayesian model evidenceor minimizing variational free energywe ask whether neuronal responses can be described as a gradient descent on variational free energy. Using a standard (Markov decision process) generative model, we derive the neuronal dynamics implicit in this description and reproduce a remarkable range of well-characterized neuronal phenomena. These include repetition suppression, mismatch negativity, violation responses, place-cell activity, phase precession, theta sequences, theta-gamma coupling, evidence accumulation, race-to-bound dynamics, and transfer of dopamine responses. Furthermore, the (approximately Bayes' optimal) behavior prescribed by these dynamics has a degree of face validity, providing a formal explanation for reward seeking, context learning, and epistemic foraging. Technically, the fact that a gradient descent appears to be a valid description of neuronal activity means that variational free energy is a Lyapunov function for neuronal dynamics, which therefore conform to Hamilton's principle of least action.