Zdeblick Daniel N, Shea-Brown Eric T, Witten Daniela M, Buice Michael A
Electrical and Computer Engineering, University of Washington.
Applied Math, University of Washington.
bioRxiv. 2023 Mar 1:2023.02.28.530327. doi: 10.1101/2023.02.28.530327.
A major goal of computational neuroscience is to build accurate models of the activity of neurons that can be used to interpret their function in circuits. Here, we explore using to refine single-cell models by grouping them into functionally relevant classes. Formally, we define a hierarchical generative model for cell types, single-cell parameters, and neural responses, and then derive an expectation-maximization algorithm with variational inference that maximizes the likelihood of the neural recordings. We apply this "simultaneous" method to estimate cell types and fit single-cell models from simulated data, and find that it accurately recovers the ground truth parameters. We then apply our approach to neural recordings from neurons in mouse primary visual cortex, and find that it yields improved prediction of single-cell activity. We demonstrate that the discovered cell-type clusters are well separated and generalizable, and thus amenable to interpretation. We then compare discovered cluster memberships with locational, morphological, and transcriptomic data. Our findings reveal the potential to improve models of neural responses by explicitly allowing for shared functional properties across neurons.
计算神经科学的一个主要目标是构建能够用于解释神经元在神经回路中功能的准确神经元活动模型。在此，我们探索通过将单细胞模型分组为功能相关的类别来对其进行优化。形式上，我们为细胞类型、单细胞参数和神经反应定义了一个层次生成模型，然后推导了一种带有变分推理的期望最大化算法，该算法使神经记录的似然性最大化。我们应用这种“同步”方法从模拟数据中估计细胞类型并拟合单细胞模型，发现它能准确恢复真实参数。然后我们将我们的方法应用于来自小鼠初级视觉皮层神经元的神经记录，发现它能改进对单细胞活动的预测。我们证明所发现的细胞类型簇是很好分离且可推广的，因此便于解释。然后我们将所发现的簇成员与位置、形态和转录组数据进行比较。我们的研究结果揭示了通过明确允许神经元之间共享功能特性来改进神经反应模型的潜力。
