Resolving Non-identifiability Mitigates Bias in Models of Neural Tuning and Functional Coupling.
Resolving Non-identifiability Mitigates Bias in Models of Neural Tuning and Functional Coupling.
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解决不可识别性可以减轻神经调节和功能耦合模型中的偏差。
DOI：
10.1101/2023.07.11.548615
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Bouchard,KristoferE
中科院分区：
文献类型：
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作者：
Sachdeva,Pratik;Bak,JiHyun;Livezey,Jesse;Kirst,Christoph;Frank,Loren;Bhattacharyya,Sharmodeep;Bouchard,KristoferE
在大脑中，所有的神经元都是由其他神经元的活动驱动的，其中一些可能同时被记录，但大多数不是。因此，神经元活动的模型需要考虑同时记录的神经元和未测量的神经元的影响。这可以通过包括观察到的外部变量的模型项来完成（例如，调谐到刺激）以及可变性的潜在来源的术语。确定相对于其他影响，神经元组彼此之间的影响对于理解大脑功能很重要。与数据拟合的统计模型的参数通常用于深入了解这些影响的相对重要性。模型的科学解释取决于无偏参数估计。然而，有偏推理的评价很少进行和偏见的来源知之甚少。通过大量的数值研究和分析计算，我们表明，常见的推理程序和模型通常是有偏见的。我们证明，准确的参数选择之前，估计解决模型的不可识别性和减轻偏见。在不同的神经生理学数据集，我们发现，耦合到其他神经元的贡献往往被高估，而调整到外源性变量被低估，在常见的方法。我们解释异质性在观察到的偏差在数据集的数据统计。最后，与常见的直觉相反，我们发现模型的不可识别性会导致偏差，而不是方差，使其成为一种特别阴险的统计错误形式。总之，我们的研究结果确定了常见神经数据模型中统计偏差的原因，提供了减轻这种偏差的推理程序，并揭示和解释了这些偏差在不同神经数据集中的影响。
In the brain, all neurons are driven by the activity of other neurons, some of which maybe simultaneously recorded, but most are not. As such, models of neuronal activity need to account for simultaneously recorded neurons and the influences of unmeasured neurons. This can be done through inclusion of model terms for observed external variables (e.g., tuning to stimuli) as well as terms for latent sources of variability. Determining the influence of groups of neurons on each other relative to other influences is important to understand brain functioning. The parameters of statistical models fit to data are commonly used to gain insight into the relative importance of those influences. Scientific interpretation of models hinge upon unbiased parameter estimates. However, evaluation of biased inference is rarely performed and sources of bias are poorly understood. Through extensive numerical study and analytic calculation, we show that common inference procedures and models are typically biased. We demonstrate that accurate parameter selection before estimation resolves model non-identifiability and mitigates bias. In diverse neurophysiology data sets, we found that contributions of coupling to other neurons are often overestimated while tuning to exogenous variables are underestimated in common methods. We explain heterogeneity in observed biases across data sets in terms of data statistics. Finally, counter to common intuition, we found that model non-identifiability contributes to bias, not variance, making it a particularly insidious form of statistical error. Together, our results identify the causes of statistical biases in common models of neural data, provide inference procedures to mitigate that bias, and reveal and explain the impact of those biases in diverse neural data sets.