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Bayesian mechanics of perceptual inference and motor control in the brain
Biological Cybernetics ( IF 1.9 ) Pub Date : 2021-01-20 , DOI: 10.1007/s00422-021-00859-9
Chang Sub Kim 1
Affiliation  

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 in Neural Comput 30:2616–2659, 2018, https://doi.org/10.1162/neco_a_01115) 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.



中文翻译:

大脑中知觉推理和运动控制的贝叶斯力学

神经科学中的自由能原理 (FEP) 规定,所有可行的药物都会诱导和最小化大脑中的信息自由能,以适应其环境生态位。在这项研究中,我们继续努力通过基于最小作用量原则实现自由能最小化,使 FEP 成为更具物理原理的形式主义。我们通过将早期出版物(Kim in Neural Comput 30:2616–2659, 2018, https://doi.org/10.1162/neco_a_01115)中报告的公式转换为考虑被动感知之外的主动推理来构建贝叶斯力学 (BM)。BM 是 FEP 下连续时间变分贝叶斯的神经实现。由此产生的 BM 作为有效的汉密尔顿运动方程提供,并受制于大脑在本体感受层面的预测误差所产生的控制信号。为了证明我们的方法的实用性,我们采用了一个简单的基于代理的模型,并通过在神经相空间中集成 BM 来展示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常见的状态空间公式进行比较,概括了 FEP 中的主要理论架构。

更新日期:2021-01-20
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