Department of Anatomy and Neurobiology, University of California Irvine Irvine, CA, USA.
Front Neural Circuits. 2012 Nov 16;6:83. doi: 10.3389/fncir.2012.00083. eCollection 2012.
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.
最近在并行计算方面的进展，包括创建神经元模拟环境的并行版本，使得神经网络模型能够达到以前无法企及的复杂和详细程度。之前，我们发布了一个具有超过 50000 个具有生理现实性的多腔室神经元的大鼠齿状回功能神经元模型，但网络模拟只能利用单个处理器。通过将模型转换为利用并行神经元，我们现在能够利用更多的计算资源，并能够模拟全规模的齿状回，其中包含超过 100 万个神经元。这消除了以前对缩放调整的必要性，并能够更直接地与实验技术和结果进行比较。向并行计算的转换提供了计算时间的超线性加速，并大大增加了模型可用的总计算机内存。额外计算资源的纳入允许模型中包含更多的细节和元素，使模型更接近对生物齿状回的更完整和准确的表示。作为向更准确地表示生物齿状回迈出的重要一步的一个例子，我们讨论了将现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、二维的树突形态，对于相同类别的神经元是相同的。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的三维颗粒细胞形态来引入细胞间的可变性。通过这些和其他改进，我们旨在构建一个更完整的大鼠齿状回全规模模型，提供一个更好的工具来描绘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。
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