Even though the brain operates in pure darkness, within the skull, it can infer the most likely causes of its sensory input. An approach to modelling this inference is to assume that the brain has a generative model of the world, which it can invert to infer the hidden causes behind its sensory stimuli, that is, perception. This assumption raises key questions: how to formulate the problem of designing brain-inspired generative models, how to invert them for the tasks of inference and learning, what is the appropriate loss function to be optimised, and, most importantly, what are the different choices of mean field approximation (MFA) and their implications for variational inference (VI).

翻译：尽管大脑在颅骨内处于完全黑暗的环境中，它仍能推断出感觉输入的最可能原因。建模这种推理的一种方法是假设大脑拥有一个世界生成模型，并通过反转该模型来推断感觉刺激背后的隐藏原因——即感知。这一假设提出了关键问题：如何构建设计脑启发生成模型的问题框架，如何反转这些模型以完成推理与学习任务，需要优化何种合适的损失函数，以及最重要的是，均值场近似（MFA）的不同选择及其对变分推理（VI）的影响。