大脑如何用概率进行计算？
How does the brain compute with probabilities?
AI总结：
本文通过生成式对抗协作，用统一语言比较概率群体编码、分布式分布编码和神经采样编码三种概率计算方案，并回顾关键经验证据、提出未来理论与实验方向。
摘要：
这篇观点文章是一个生成式对抗协作（GAC）的成果，旨在探讨“神经活动如何表示概率分布？”这一问题。我们解决了在回答该问题方面取得进展所面临的三个主要障碍：首先，我们提供了一种统一的语言来定义相互竞争的假设。其次，我们用这种共同语言解释了三种重要的概率计算方案的基本原理——概率群体编码（PPCs）、分布式分布编码（DDCs）和神经采样编码（NSCs）——并描述了它们之间的异同。第三，我们回顾了此前被视为至少支持其中一种方案的关键经验数据，并描述了这些数据是否可能或不可能被其他方案所解释。最后，我们描述了解决这一争论所面临的一些关键挑战，并提出了通过理论与实验相结合来应对这些挑战的潜在方向。
英文摘要：
This perspective piece is the result of a Generative Adversarial Collaboration (GAC) tackling the question `How does neural activity represent probability distributions?'. We have addressed three major obstacles to progress on answering this question: first, we provide a unified language for defining competing hypotheses. Second, we explain the fundamentals of three prominent proposals for probabilistic computations -- Probabilistic Population Codes (PPCs), Distributed Distributional Codes (DDCs), and Neural Sampling Codes (NSCs) -- and describe similarities and differences in that common language. Third, we review key empirical data previously taken as evidence for at least one of these proposal, and describe how it may or may not be explainable by alternative proposals. Finally, we describe some key challenges in resolving the debate, and propose potential directions to address them through a combination of theory and experiments.
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。
