Department of Neurobiology, University of Pennsylvania, Philadelphia, PA, USA.
Department of Computer Science, Rochester Institute of Technology, Rochester, NY, USA.
Nat Neurosci. 2023 Dec;26(12):2063-2072. doi: 10.1038/s41593-023-01458-6. Epub 2023 Nov 23.
The Bayesian brain hypothesis is one of the most influential ideas in neuroscience. However, unstated differences in how Bayesian ideas are operationalized make it difficult to draw general conclusions about how Bayesian computations map onto neural circuits. Here, we identify one such unstated difference: some theories ask how neural circuits could recover information about the world from sensory neural activity (Bayesian decoding), whereas others ask how neural circuits could implement inference in an internal model (Bayesian encoding). These two approaches require profoundly different assumptions and lead to different interpretations of empirical data. We contrast them in terms of motivations, empirical support and relationship to neural data. We also use a simple model to argue that encoding and decoding models are complementary rather than competing. Appreciating the distinction between Bayesian encoding and Bayesian decoding will help to organize future work and enable stronger empirical tests about the nature of inference in the brain.
贝叶斯大脑假说（Bayesian brain hypothesis）是神经科学中最具影响力的思想之一。然而，由于贝叶斯思想的操作方式存在未明的差异，因此很难得出关于贝叶斯计算如何映射到神经回路的一般性结论。在这里，我们确定了这样一个未阐明的差异：一些理论询问神经回路如何从感觉神经活动中恢复有关世界的信息（贝叶斯解码），而另一些理论则询问神经回路如何在内部模型中实现推理（贝叶斯编码）。这两种方法需要截然不同的假设，并导致对经验数据的不同解释。我们从动机、经验支持以及与神经数据的关系方面对它们进行了对比。我们还使用一个简单的模型来论证，编码和解码模型是互补的，而不是相互竞争的。理解贝叶斯编码和贝叶斯解码之间的区别将有助于组织未来的工作，并能够对大脑中的推理本质进行更有力的经验测试。
