Normative and task-driven theories offer powerful top-down explanations for biological systems, yet the goals of quantitatively arbitrating between competing theories, and utilizing them as inductive biases to improve data-driven fits of real biological datasets are prohibitively laborious, and often impossible. To this end, we introduce a Bayesian meta-learning framework designed to automatically convert raw functional predictions from normative theories into tractable probabilistic models. We employ adaptive deep kernel Gaussian processes, meta-learning a kernel on synthetic data generated from a normative theory. This Theory-Informed Kernel specifies a probabilistic model representing the theory predictions -- usable for both fitting data and rigorously validating the theory. As a demonstration, we apply our framework to the early visual system, using efficient coding as our normative theory. We show improved response prediction accuracy in ex vivo recordings of mouse retinal ganglion cells stimulated by natural scenes compared to conventional data-driven baselines, while providing well-calibrated uncertainty estimates and interpretable representations. Using exact Bayesian model selection, we also show that our informed kernel can accurately infer the degree of theory-match from data, confirming faithful encapsulation of theory structure. This work provides a more general, scalable, and automated approach for integrating theoretical knowledge into data-driven scientific inquiry in neuroscience and beyond.

翻译：规范性与任务驱动理论为生物系统提供了强大的自上而下解释，然而定量仲裁竞争理论、以及将其用作归纳偏置以改进真实生物数据集的数据驱动拟合这两个目标，其实现过程极其繁重且往往不可行。为此，我们引入了一个贝叶斯元学习框架，旨在自动将规范性理论的原始功能预测转化为可处理的概率模型。我们采用自适应深度核高斯过程，在规范性理论生成的合成数据上元学习一个核函数。该理论引导核定义了一个表征理论预测的概率模型——既可用于数据拟合，也可用于严格验证理论。作为演示，我们将本框架应用于早期视觉系统，以高效编码作为规范性理论。实验表明，相较于传统数据驱动基线方法，在自然场景刺激下的小鼠视网膜神经节细胞离体记录中，我们的方法提高了响应预测精度，同时提供了校准良好的不确定性估计与可解释的表征。通过精确贝叶斯模型选择，我们还证明该引导核能够从数据中准确推断理论匹配度，从而确认其对理论结构的忠实封装。这项工作为神经科学及其他领域提供了一种更通用、可扩展且自动化的方法，将理论知识整合到数据驱动的科学探究中。