Statistical inference on representational geometries.
Statistical inference on representational geometries.
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DOI：
10.7554/elife.82566
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发表时间：
2023-08-23
期刊：
影响因子：
7.7
通讯作者：
Kriegeskorte N
中科院分区：
文献类型：
--
作者：
Schütt HH;Kipnis AD;Diedrichsen J;Kriegeskorte N
神经科学最近取得了很大进展，扩大了神经活动测量和大脑计算模型的复杂性。然而，我们缺乏强大的方法来通过使用新的大数据评估我们的新的大模型来连接理论和实验。在这里，我们介绍了新的推理方法，使研究人员能够评估和比较模型的基础上，他们的代表性几何预测的准确性：一个好的模型应该准确地预测之间的距离神经群体表示（例如，一组刺激）。我们的推理方法结合了交叉验证的联合收割机新的2因子扩展（以防止过度拟合的主题或条件膨胀我们的估计模型的准确性）和自举（使推理模型比较与同时推广到新的主题和新的条件）。我们通过使用深度神经网络模拟数据以及通过钙成像和功能性MRI数据的恢复来验证地面真实模型已知的数据上的推理方法。结果表明，该方法是有效的，结论是正确的推广。这些数据分析方法可以在开源Python工具箱（rsatoolbox.readthedocs.io）中找到。
Neuroscience has recently made much progress, expanding the complexity of both neural activity measurements and brain-computational models. However, we lack robust methods for connecting theory and experiment by evaluating our new big models with our new big data. Here, we introduce new inference methods enabling researchers to evaluate and compare models based on the accuracy of their predictions of representational geometries: A good model should accurately predict the distances among the neural population representations (e.g. of a set of stimuli). Our inference methods combine novel 2-factor extensions of crossvalidation (to prevent overfitting to either subjects or conditions from inflating our estimates of model accuracy) and bootstrapping (to enable inferential model comparison with simultaneous generalization to both new subjects and new conditions). We validate the inference methods on data where the ground-truth model is known, by simulating data with deep neural networks and by resampling of calcium-imaging and functional MRI data. Results demonstrate that the methods are valid and conclusions generalize correctly. These data analysis methods are available in an open-source Python toolbox (rsatoolbox.readthedocs.io).