Allen Institute for Brain Science, 615 Westlake Ave N, Seattle, WA, 98109, USA.
Howard Hughes Medical Institute, Janelia Research Campus, 19700 Helix Dr, Ashburn, VA, 20147, USA.
Nat Commun. 2018 Feb 19;9(1):709. doi: 10.1038/s41467-017-02717-4.
There is a high diversity of neuronal types in the mammalian neocortex. To facilitate construction of system models with multiple cell types, we generate a database of point models associated with the Allen Cell Types Database. We construct a set of generalized leaky integrate-and-fire (GLIF) models of increasing complexity to reproduce the spiking behaviors of 645 recorded neurons from 16 transgenic lines. The more complex models have an increased capacity to predict spiking behavior of hold-out stimuli. We use unsupervised methods to classify cell types, and find that high level GLIF model parameters are able to differentiate transgenic lines comparable to electrophysiological features. The more complex model parameters also have an increased ability to differentiate between transgenic lines. Thus, creating simple models is an effective dimensionality reduction technique that enables the differentiation of cell types from electrophysiological responses without the need for a priori-defined features. This database will provide a set of simplified models of multiple cell types for the community to use in network models.
哺乳动物新皮层中有高度多样化的神经元类型。为了方便构建具有多种细胞类型的系统模型，我们生成了一个与艾伦细胞类型数据库相关的点模型数据库。我们构建了一组具有越来越复杂的广义漏电积分和放电（GLIF）模型，以再现 16 个转基因系中 645 个记录神经元的尖峰行为。更复杂的模型具有增加的预测保持刺激尖峰行为的能力。我们使用无监督方法对细胞类型进行分类，发现高级 GLIF 模型参数能够区分转基因系，与电生理特征相当。更复杂的模型参数也具有增加的区分转基因系的能力。因此，创建简单的模型是一种有效的降维技术，能够在不需要先验定义特征的情况下，从电生理反应中区分细胞类型。该数据库将为社区提供一组简化的多种细胞类型模型，用于网络模型。
