Institute of Science and Technology Austria, Am Campus 1, 3400 Klosterneuburg, Austria.
Institute of Science and Technology Austria, Am Campus 1, 3400 Klosterneuburg, Austria.
Neuron. 2021 Apr 7;109(7):1227-1241.e5. doi: 10.1016/j.neuron.2021.01.020. Epub 2021 Feb 15.
Normative theories and statistical inference provide complementary approaches for the study of biological systems. A normative theory postulates that organisms have adapted to efficiently solve essential tasks and proceeds to mathematically work out testable consequences of such optimality; parameters that maximize the hypothesized organismal function can be derived ab initio, without reference to experimental data. In contrast, statistical inference focuses on the efficient utilization of data to learn model parameters, without reference to any a priori notion of biological function. Traditionally, these two approaches were developed independently and applied separately. Here, we unify them in a coherent Bayesian framework that embeds a normative theory into a family of maximum-entropy "optimization priors." This family defines a smooth interpolation between a data-rich inference regime and a data-limited prediction regime. Using three neuroscience datasets, we demonstrate that our framework allows one to address fundamental challenges relating to inference in high-dimensional, biological problems.
规范理论和统计推断为生物系统的研究提供了互补的方法。规范理论假设生物体已经适应了高效地解决基本任务，并从数学上推导出这种最优性的可测试结果；可以从初始参数中推导出最大化假设生物体功能的参数，而无需参考实验数据。相比之下，统计推断则侧重于有效地利用数据来学习模型参数，而无需参考任何关于生物功能的先验概念。传统上，这两种方法是独立开发并分别应用的。在这里，我们将它们统一在一个连贯的贝叶斯框架中，将规范理论嵌入到最大熵“优化先验”的家族中。这个家族定义了在数据丰富的推断模式和数据有限的预测模式之间的平滑插值。我们使用三个神经科学数据集证明，我们的框架可以解决与高维生物问题推断相关的基本挑战。
