Since their inception, computational models have become increasingly complex and useful counterparts to laboratory experiments within the field of neuroscience. Today several software programs exist to solve the underlying mathematical system of equations, but such programs typically solve these equations in all parts of a cell (or network of cells) simultaneously, regardless of whether or not all of the cell is active. This approach can be inefficient if only part of the cell is active and many simulations must be performed. We have previously developed a numerical method that provides a framework for spatial adaptivity by making the computations local to individual branches rather than entire cells (Rempe and Chopp, SIAM Journal on Scientific Computing, 28: 2139-2161, 2006). Once the computation is reduced to the level of branches instead of cells, spatial adaptivity is straightforward: the active regions of the cell are detected and computational effort is focused there, while saving computations in other regions of the cell that are at or near rest. Here we apply the adaptive method to four realistic neuronal simulation scenarios and demonstrate its improved efficiency over non-adaptive methods. We find that the computational cost of the method scales with the amount of activity present in the simulation, rather than the physical size of the system being simulated. For certain problems spatial adaptivity reduces the computation time by up to 80%.

中文翻译：

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具有空间适应性的房室神经模拟。

自成立以来，计算模型已变得越来越复杂和有用，可作为神经科学领域实验室实验的对应物。今天，存在若干软件程序来求解方程的基本数学系统，但此类程序通常同时求解单元（或单元网络）的所有部分中的这些方程，而不管所有单元是否处于活动状态。如果只有部分单元处于活动状态并且必须执行许多模拟，则此方法可能效率低下。我们之前开发了一种数值方法，该方法通过对单个分支而不是整个单元进行本地计算来提供空间适应性框架（Rempe 和 Chopp，SIAM Journal on Scientific Computing，28：2139-2161，2006）。一旦计算减少到分支而不是单元的级别，空间适应性就很简单：检测单元的活动区域并将计算工作集中在那里，同时节省处于或接近静止状态的其他单元区域的计算。在这里，我们将自适应方法应用于四个现实的神经元模拟场景，并证明其比非自适应方法提高了效率。我们发现该方法的计算成本与模拟中存在的活动量成比例，而不是被模拟系统的物理大小。对于某些问题，空间适应性可将计算时间减少多达 80%。同时保存细胞其他处于或接近静止状态的区域的计算。在这里，我们将自适应方法应用于四个现实的神经元模拟场景，并证明其比非自适应方法提高了效率。我们发现该方法的计算成本与模拟中存在的活动量成比例，而不是被模拟系统的物理大小。对于某些问题，空间适应性可将计算时间减少多达 80%。同时保存细胞其他处于或接近静止状态的区域的计算。在这里，我们将自适应方法应用于四个现实的神经元模拟场景，并证明其比非自适应方法提高了效率。我们发现该方法的计算成本与模拟中存在的活动量成比例，而不是被模拟系统的物理大小。对于某些问题，空间适应性可将计算时间减少多达 80%。