RIKEN Center for Brain Science, Wako-shi, Saitama, Japan.
Centre de Recherche Cerveau et Cognition, CNRS, Université de Toulouse, Toulouse, France.
PLoS Comput Biol. 2024 Oct 15;20(10):e1012478. doi: 10.1371/journal.pcbi.1012478. eCollection 2024 Oct.
In natural behaviors, multiple neural signals simultaneously drive activation across overlapping brain networks. Due to limitations in the amount of data that can be acquired in common experimental designs, the determination of these interactions is commonly inferred via modeling approaches, which reduce overfitting by finding appropriate regularizing hyperparameters. However, it is unclear whether these hyperparameters can also be related to any aspect of the underlying biological phenomena and help interpret them. We applied a state-of-the-art regularization procedure-automatic locality determination-to interacting neural activations in the mouse posterior cortex associated with movements of the body and eyes. As expected, regularization significantly improved the determination and interpretability of the response interactions. However, regularizing hyperparameters also changed considerably, and seemingly unpredictably, from animal to animal. We found that these variations were not random; rather, they correlated with the variability in visually evoked responses and with the variability in the state of arousal of the animals measured by pupillometry-both pieces of information that were not included in the modeling framework. These observations could be generalized to another commonly used-but potentially less informative-regularization method, ridge regression. Our findings demonstrate that optimal model hyperparameters can be discovery tools that are informative of factors not a priori included in the model's design.
在自然行为中，多个神经信号同时驱动重叠脑网络的激活。由于在常见实验设计中可以获取的数据量有限，这些相互作用的确定通常通过建模方法推断得出，这些方法通过找到适当的正则化超参数来减少过拟合。然而，目前尚不清楚这些超参数是否也可以与潜在生物学现象的任何方面相关，并有助于解释它们。我们应用了一种最先进的正则化程序——自动局部确定——来处理与身体和眼睛运动相关的小鼠后皮质中的相互作用神经激活。正如预期的那样，正则化显著提高了响应相互作用的确定和可解释性。然而，正则化超参数也在动物之间发生了相当大的变化，而且似乎是不可预测的。我们发现，这些变化并不是随机的；相反，它们与视觉诱发反应的可变性以及通过瞳孔测量法测量的动物觉醒状态的可变性相关——这两种信息都不包括在建模框架中。这些观察结果可以推广到另一种常用的但可能信息量较少的正则化方法——岭回归。我们的研究结果表明，最优模型超参数可以成为发现工具，这些工具可以提供模型设计中未预先包含的因素的信息。
