典型皮层回路与贝叶斯推断和最优控制的对偶性
Canonical Cortical Circuits and the Duality of Bayesian Inference and Optimal Control
摘要：
自20世纪60年代以来，感觉推理和运动控制的双重性就已为人所知，最近被认为是贝叶斯推断中后验分布和最优控制中值函数所需计算的共性。与此同时，关于大脑的一个有趣的问题是，为什么整个新皮层共享一个典型的六层结构，而它的后半部分和前半部分分别参与感觉处理和运动控制。在这里，我们考虑的假设，感觉和运动皮层电路实现双重计算的贝叶斯推断和最优控制，或感知和价值为基础的决策，分别。我们首先回顾了线性二次系统中推理和控制的经典对偶性，然后回顾了动态贝叶斯推断和最优控制之间的对应关系。基于典型的皮层电路的架构，我们探讨了不同的皮层神经元如何表示变量和实现计算。
英文摘要：
The duality of sensory inference and motor control has been known since the 1960s and has recently been recognized as the commonality in computations required for the posterior distributions in Bayesian inference and the value functions in optimal control. Meanwhile, an intriguing question about the brain is why the entire neocortex shares a canonical six-layer architecture while its posterior and anterior halves are engaged in sensory processing and motor control, respectively. Here we consider the hypothesis that the sensory and motor cortical circuits implement the dual computations for Bayesian inference and optimal control, or perceptual and value-based decision making, respectively. We first review the classic duality of inference and control in linear quadratic systems and then review the correspondence between dynamic Bayesian inference and optimal control. Based on the architecture of the canonical cortical circuit, we explore how different cortical neurons may represent variables and implement computations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
