Gouwens Nathan W, Berg Jim, Feng David, Sorensen Staci A, Zeng Hongkui, Hawrylycz Michael J, Koch Christof, Arkhipov Anton
Allen Institute for Brain Science, 615 Westlake Avenue N, Seattle, WA, 98109, USA.
Nat Commun. 2018 Feb 19;9(1):710. doi: 10.1038/s41467-017-02718-3.
The cellular components of mammalian neocortical circuits are diverse, and capturing this diversity in computational models is challenging. Here we report an approach for generating biophysically detailed models of 170 individual neurons in the Allen Cell Types Database to link the systematic experimental characterization of cell types to the construction of cortical models. We build models from 3D morphologies and somatic electrophysiological responses measured in the same cells. Densities of active somatic conductances and additional parameters are optimized with a genetic algorithm to match electrophysiological features. We evaluate the models by applying additional stimuli and comparing model responses to experimental data. Applying this technique across a diverse set of neurons from adult mouse primary visual cortex, we verify that models preserve the distinctiveness of intrinsic properties between subsets of cells observed in experiments. The optimized models are accessible online alongside the experimental data. Code for optimization and simulation is also openly distributed.
哺乳动物新皮层回路的细胞成分多种多样，在计算模型中捕捉这种多样性具有挑战性。在此，我们报告了一种方法，用于在艾伦细胞类型数据库中生成170个单个神经元的生物物理详细模型，以将细胞类型的系统实验表征与皮层模型的构建联系起来。我们根据在同一细胞中测量的三维形态和体细胞电生理反应构建模型。利用遗传算法优化活跃体细胞电导的密度和其他参数，以匹配电生理特征。我们通过施加额外刺激并将模型反应与实验数据进行比较来评估模型。将该技术应用于成年小鼠初级视觉皮层的各种神经元，我们验证了模型保留了实验中观察到的细胞亚群之间内在特性的独特性。优化后的模型可与实验数据一起在线获取。优化和模拟代码也已公开分发。
