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Abstract：
It is widely believed that the brain performs approximate probabilistic inference to estimate causal variables in the world from ambiguous sensory data. To understand these computations, we need to analyze how information is represented and transformed by the actions of nonlinear recurrent neural networks. We propose that these probabilistic computations function by a message-passing algorithm operating at the level of redundant neural populations. To explain this framework, we review its underlying concepts, including graphical models, sufficient statistics, and message-passing, and then describe how these concepts could be implemented by recurrently connected probabilistic population codes. The relevant information flow in these networks will be most interpretable at the population level, particularly for redundant neural codes. We therefore outline a general approach to identify the essential features of a neural message-passing algorithm. Finally, we argue that to reveal the most important aspects of these neural computations, we must study large-scale activity patterns during moderately complex, naturalistic behaviors.
摘要：
人们普遍认为，大脑会进行近似的概率推理，以从模糊的感觉数据中估计世界上的因果变量。为了理解这些计算，我们需要分析如何通过非线性递归神经网络的作用来表示和转换信息。我们建议这些概率计算通过在冗余神经种群级别上运行的消息传递算法起作用。为了解释这个框架，我们回顾它的基本概念，包括图形模型，足够的统计数据，和消息传递，然后描述如何通过递归连接的概率种群代码来实现这些概念。这些网络中的相关信息流将在人口层面上最容易解释，特别是对于冗余的神经代码。因此，我们概述了一种识别神经消息传递算法基本特征的通用方法。最后,我们认为，为了揭示这些神经计算的最重要方面，我们必须研究中等复杂时期的大规模活动模式，自然主义行为。
