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Compartmental neural simulations with spatial adaptivity

机译：具有空间适应性的区室神经模拟

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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, SUM 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%.
机译：自从它们诞生以来，计算模型已经变得越来越复杂，并且在神经科学领域已经成为实验室实验的有用对象。如今，存在一些软件程序来求解基本的方程式数学系统，但是此类程序通常在单元格（或单元格网络）的所有部分同时求解这些方程式，而不管是否所有单元格都处于活动状态。如果仅单元的一部分处于活动状态并且必须执行许多模拟，则此方法可能效率不高。我们之前已经开发了一种数值方法，该方法通过使计算局部于单个分支而不是整个单元来为空间适应性提供框架（Rempe和Chopp，SUM Journal on Scientific Computing，28：2139-2161，2006）。一旦将计算减少到分支级别而不是单元级别，空间适应性就会变得很简单：检测到单元的活动区域并将计算工作集中在此，同时将计算保存在单元中处于静止或接近静止的其他区域。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具有更高的效率。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，而不是与要模拟的系统的物理大小成比例。对于某些问题，空间适应性最多可减少80％的计算时间。

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