Polynomial, piecewise-Linear, Step (PLS): A Simple, Scalable, and Efficient Framework for Modeling Neurons.
Polynomial, piecewise-Linear, Step (PLS): A Simple, Scalable, and Efficient Framework for Modeling Neurons.
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DOI：
10.3389/fninf.2021.642933
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发表时间：
2021
影响因子：
3.5
通讯作者：
Colonnese MT
中科院分区：
文献类型：
--
作者：
Tikidji-Hamburyan RA;Colonnese MT
生物神经元可以用不同级别的生物物理/生化细节进行建模。模型反映实际生理过程以及最终神经元信息功能的准确性可以从非常详细到示意性的现象学表示。这个范围的存在是由于一个常见问题：需要在捕获神经元中必要的信息处理所需的细节水平与计算 1 秒模型时间所需的计算负载之间找到最佳权衡。模型网络规模或模型时间的增加（应获得解决方案）使得这种权衡在模型开发中变得至关重要。当需要在很长的时间间隔内对具有每个神经元的详细表示的广泛网络进行建模以研究缓慢的进化过程（例如丘脑皮质回路的发育）时，数值模拟变得非常具有挑战性。在这里，我们提出了一种简单、强大且灵活的方法，其中我们通过以下三个系列的函数组合来近似微分方程的右侧：多项式、分段线性、步长 (PLS)。为了获得单一的连贯框架，我们提供了应组合 PLS 功能的四个核心原则。我们展示了每项核心原则背后的基本原理。两个示例说明了如何使用 PLS 框架构建基于电导的模型或唯象模型。我们使用第一个示例作为三个不同计算平台的基准：CPU、GPU 和移动片上系统设备。我们证明，PLS 框架可以在不增加内存占用的情况下加快计算速度，并保持与完全计算模型或查找表近似相当的高模型保真度。我们相信，从生物物理到现象学甚至抽象模型的所有神经元模型都可以从使用 PLS 框架中受益。
Biological neurons can be modeled with different levels of biophysical/biochemical details. The accuracy with which a model reflects the actual physiological processes and ultimately the information function of a neuron, can range from very detailed to a schematic phenomenological representation. This range exists due to the common problem: one needs to find an optimal trade-off between the level of details needed to capture the necessary information processing in a neuron and the computational load needed to compute 1 s of model time. An increase in modeled network size or model-time, for which the solution should be obtained, makes this trade-off pivotal in model development. Numerical simulations become incredibly challenging when an extensive network with a detailed representation of each neuron needs to be modeled over a long time interval to study slow evolving processes, e.g., development of the thalamocortical circuits. Here we suggest a simple, powerful and flexible approach in which we approximate the right-hand sides of differential equations by combinations of functions from three families: Polynomial, piecewise-Linear, Step (PLS). To obtain a single coherent framework, we provide four core principles in which PLS functions should be combined. We show the rationale behind each of the core principles. Two examples illustrate how to build a conductance-based or phenomenological model using the PLS-framework. We use the first example as a benchmark on three different computational platforms: CPU, GPU, and mobile system-on-chip devices. We show that the PLS-framework speeds up computations without increasing the memory footprint and maintains high model fidelity comparable to the fully-computed model or with lookup-table approximation. We are convinced that the full range of neuron models: from biophysical to phenomenological and even to abstract models, may benefit from using the PLS-framework.
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