T2N as a new tool for robust electrophysiological modeling demonstrated for mature and adult-born dentate granule cells.
T2N as a new tool for robust electrophysiological modeling demonstrated for mature and adult-born dentate granule cells.
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DOI：
10.7554/elife.26517
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发表时间：
2017-11-22
期刊：
影响因子：
7.7
通讯作者：
Jedlicka P
中科院分区：
文献类型：
--
作者：
Beining M;Mongiat LA;Schwarzacher SW;Cuntz H;Jedlicka P
区隔模型是理解单个神经元计算的首选理论工具。然而，许多模型是不完整的，是临时构建的，需要针对每个新条件进行调整，从而使它们的可用性受到限制。在这里，我们提出T2N，一个强大的接口来控制神经元与Matlab和TREES工具箱，它支持生成稳定的模型在广泛的重构和合成形态。我们用一种新颖的、高度详细的齿状颗粒细胞（GCs）活性模型来说明这一点，该模型复制了来自各个实验室的广泛实验。通过实现离子通道组成和形态的已知差异，我们的模型再现了小鼠或大鼠，成熟或成年出生的GCs以及药物干预和癫痫状况的数据。这项工作为详细的分区建模设定了一个新的基准。T2N适用于创建对大规模网络有用的稳健模型，从而产生新的预测。我们讨论了T2N在简并研究中的可能应用。
Compartmental models are the theoretical tool of choice for understanding single neuron computations. However, many models are incomplete, built ad hoc and require tuning for each novel condition rendering them of limited usability. Here, we present T2N, a powerful interface to control NEURON with Matlab and TREES toolbox, which supports generating models stable over a broad range of reconstructed and synthetic morphologies. We illustrate this for a novel, highly detailed active model of dentate granule cells (GCs) replicating a wide palette of experiments from various labs. By implementing known differences in ion channel composition and morphology, our model reproduces data from mouse or rat, mature or adult-born GCs as well as pharmacological interventions and epileptic conditions. This work sets a new benchmark for detailed compartmental modeling. T2N is suitable for creating robust models useful for large-scale networks that could lead to novel predictions. We discuss possible T2N application in degeneracy studies.