论文标题
大脑感知推理和运动控制的贝叶斯力学
Bayesian mechanics of perceptual inference and motor control in the brain
论文作者
论文摘要
神经科学中的自由能原理（FEP）规定，所有可行的药物都诱导并最大程度地减少大脑中的信息自由能，以适合其环境利基市场。在这项研究中，我们继续努力通过基于最小动作的原则来实施自由能的最小化，使FEP成为更有原则的形式主义。我们通过施放早期出版物（Kim 2018）中报道的表述来建立贝叶斯力学（BM），以考虑超越被动感知的主动推断。 BM是连续时间下FEP下变异贝叶斯的神经实施。所得的BM作为有效的汉密尔顿运动方程式提供，并受到大脑在本体感受水平的预测错误产生的控制信号。为了证明我们方法的实用性，我们采用了一个简单的基于代理的模型，并通过将BM整合在神经相空间中，对大脑执行识别动力学的具体数值说明。此外，我们通过将我们的方法与共同的状态空间公式进行比较，从而概括了FEP中的主要理论体系结构。
The free energy principle (FEP) in the neurosciences stipulates that all viable agents induce and minimize informational free energy in the brain to fit their environmental niche. In this study, we continue our effort to make the FEP a more physically principled formalism by implementing free energy minimization based on the principle of least action. We build a Bayesian mechanics (BM) by casting the formulation reported in the earlier publication (Kim 2018) to considering active inference beyond passive perception. The BM is a neural implementation of variational Bayes under the FEP in continuous time. The resulting BM is provided as an effective Hamilton's equation of motion and subject to the control signal arising from the brain's prediction errors at the proprioceptive level. To demonstrate the utility of our approach, we adopt a simple agent-based model and present a concrete numerical illustration of the brain performing recognition dynamics by integrating BM in neural phase space. Furthermore, we recapitulate the major theoretical architectures in the FEP by comparing our approach with the common state-space formulations.
