解析大脑如何用神经活动表示概率分布
How does the brain compute with probabilities?
- 构建统一语言描述三种概率计算假说
- 梳理概率编码、分布编码、采样编码的核心机制
- 适合认知神经科学与计算神经模型研究者
本文是通过生成对抗协作（GAC）探讨「神经活动如何表示概率分布」这一问题的视角性论文。针对该问题的研究进展，我们克服了三大障碍：首先，提出一种统一的语言来定义竞争性假说；其次，阐述三种主流的概率计算模型——概率种群编码（PPCs）、分布式分布编码（DDCs）和神经采样编码（NSCs）的基本原理，并在统一框架下比较其异同；第三，回顾以往支持其中某一假说的关键实证数据，并分析这些数据是否可被其他假说解释；最后，指出当前争议的关键挑战，并提出结合理论与实验的潜在解决路径。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。