Toward a full-scale computational model of the rat dentate gyrus.
Toward a full-scale computational model of the rat dentate gyrus.
复制标题
迈向大鼠齿状回的全尺度计算模型。
DOI：
10.3389/fncir.2012.00083
复制
发表时间：
2012
影响因子：
3.5
通讯作者：
Soltesz I
中科院分区：
文献类型：
--
作者：
Schneider CJ;Bezaire M;Soltesz I
并行计算的最新进展，包括创建并行版本的 NEURON 模拟环境，使得神经网络模型的复杂性和细节达到了前所未有的水平。此前，我们发布了大鼠齿状回的功能性神经元模型，该模型具有超过 50,000 个生物物理真实的多室神经元，但网络模拟只能使用单个处理器。通过转换模型以利用并行神经元，我们现在能够利用更多的计算资源，并能够模拟包含超过一百万个神经元的全尺寸齿状回。这消除了以前进行缩放调整的必要性，并允许与实验技术和结果进行更直接的比较。并行计算的转换提供了计算时间的超线性加速，并显着增加了模型可用的整体计算机内存。额外计算资源的结合允许在模型中包含更多细节和元素，使模型更接近于生物齿状回的更完整和准确的表示。作为朝着日益准确地表示生物齿状回迈出的重要一步的一个例子，我们讨论了将真实的颗粒细胞树突纳入模型中。我们之前的模型包含简化的二维树突形态，这些形态对于同类神经元来说是相同的。使用 L-Neuron 和 L-Measure 软件工具，我们能够通过生成基于生物重建的详细三维颗粒细胞形态来引入细胞间的变异性。通过这些和其他改进，我们的目标是构建一个更完整的大鼠齿状回全尺寸模型，以提供更好的工具来描述齿状回内细胞类型的功能作用及其在癫痫中观察到的病理变化。
Recent advances in parallel computing, including the creation of the parallel version of the NEURON simulation environment, have allowed for a previously unattainable level of complexity and detail in neural network models. Previously, we published a functional NEURON model of the rat dentate gyrus with over 50,000 biophysically realistic, multicompartmental neurons, but network simulations could only utilize a single processor. By converting the model to take advantage of parallel NEURON, we are now able to utilize greater computational resources and are able to simulate the full-scale dentate gyrus, containing over a million neurons. This has eliminated the previous necessity for scaling adjustments and allowed for a more direct comparison to experimental techniques and results. The translation to parallel computing has provided a superlinear speedup of computation time and dramatically increased the overall computer memory available to the model. The incorporation of additional computational resources has allowed for more detail and elements to be included in the model, bringing the model closer to a more complete and accurate representation of the biological dentate gyrus. As an example of a major step toward an increasingly accurate representation of the biological dentate gyrus, we discuss the incorporation of realistic granule cell dendrites into the model. Our previous model contained simplified, two-dimensional dendritic morphologies that were identical for neurons of the same class. Using the software tools L-Neuron and L-Measure, we are able to introduce cell-to-cell variability by generating detailed, three-dimensional granule cell morphologies that are based on biological reconstructions. Through these and other improvements, we aim to construct a more complete full-scale model of the rat dentate gyrus, to provide a better tool to delineate the functional role of cell types within the dentate gyrus and their pathological changes observed in epilepsy.
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