黑暗中的大脑：神经拟态学习与推理的设计原则
Brain in the Dark: Design Principles for Neuro-mimetic Learning and Inference
AI总结：
本文围绕大脑在无外部光照下通过生成模型反演实现感知的机制，探讨类脑生成模型设计、推理与学习反演、损失函数选择及不同MFA方案对VI的影响等核心原则。
摘要：
尽管大脑在颅骨内处于纯粹的黑暗之中，它仍能推断出其感觉输入最可能的原因。对这种推理进行建模的一种方法，是假设大脑拥有一个关于世界的生成模型，并能够对该模型进行反演，以推断感觉刺激背后的隐藏原因，也就是感知。这一假设引出了若干关键问题：如何形式化设计受大脑启发的生成模型这一问题；如何为推理和学习任务对这些模型进行反演；何种损失函数适合被优化；以及最重要的是，平均场近似（MFA）有哪些不同选择，它们对变分推理（VI）又有何影响。
英文摘要：
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).
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。
