Simulation-based Bayesian inference (SBI) can be used to estimate the parameters of complex mechanistic models given observed model outputs without requiring access to explicit likelihood evaluations. A prime example for the application of SBI in neuroscience involves estimating the parameters governing the response dynamics of Hodgkin-Huxley (HH) models from electrophysiological measurements, by inferring a posterior over the parameters that is consistent with a set of observations. To this end, many SBI methods employ a set of summary statistics or scientifically interpretable features to estimate a surrogate likelihood or posterior. However, currently, there is no way to identify how much each summary statistic or feature contributes to reducing posterior uncertainty. To address this challenge, one could simply compare the posteriors with and without a given feature included in the inference process. However, for large or nested feature sets, this would necessitate repeatedly estimating the posterior, which is computationally expensive or even prohibitive. Here, we provide a more efficient approach based on the SBI method neural likelihood estimation (NLE): We show that one can marginalize the trained surrogate likelihood post-hoc before inferring the posterior to assess the contribution of a feature. We demonstrate the usefulness of our method by identifying the most important features for inferring parameters of an example HH neuron model. Beyond neuroscience, our method is generally applicable to SBI workflows that rely on data features for inference used in other scientific fields.
翻译:可以用基于模拟贝叶斯推断法(SBI)来估计观察到的模型产出的复杂机械模型的参数,而不需要明确的概率评估。在神经科学中应用履行机构的一个首要实例是,通过对符合一组观察的参数进行推断,从电子生理测量中推断出Hodgkin-Huxley(HHH)模型的反应动态参数,从而推断出符合一套观察的参数的子集;为此,履行机构的许多方法采用一套摘要统计或可科学解释的特征来估计代用或后游。然而,目前无法确定每个摘要统计或特征在多大程度上有助于减少后游不确定性。为了应对这一挑战,可以简单地将Hodgkin-Huxley(HHHH)模型的响应动态参数参数与符合电物理测量过程的参数的参数进行比较。然而,对于大型或嵌套的特性,则需要反复估计符合计算成本甚高甚高甚至令人望而令人望重的后。这里,我们根据履行机构方法的神经风险评估提供了一种更高效的方法(NLE):我们表明,在经过培训的神经特征依赖性模型中,在通常情况下可以将我们用于评估的神经科学特性下层模型中的一种方法,在我们用来显示我们用于评估之前的模型中的一种方法的重要用途。