Accelerating biophysical neural network simulation with region of interest based approximation
Accelerating biophysical neural network simulation with region of interest based approximation
复制标题
使用基于感兴趣区域的近似加速生物物理神经网络模拟
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
S. Mukhopadhyay
中科院分区：
文献类型：
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作者：
Yun Long;Xueyuan She;S. Mukhopadhyay
对生物物理神经网络（BNN）的动力学进行建模对于理解大脑运作和设计认知系统至关重要。大规模且符合生物物理实际的BNN建模需要求解多项、耦合的非线性微分方程，这使得模拟在计算上复杂且内存密集。本文提出了一种自适应模拟方法，其中感兴趣区域（ROI）内的神经元遵循高度符合生物学的精确模型，而其他神经元则遵循便于计算的模型。为了实现基于ROI的近似，我们提出了一种基于通用模板的计算算法，该算法统一了各种神经元模型的数据结构和计算流程。我们在CPU、GPU和嵌入式平台上实现了这些算法，结果显示在感兴趣区域内，在生物细节损失极小的情况下实现了11倍的加速。
Modeling the dynamics of biophysical neural network (BNN) is essential to understand brain operation and design cognitive systems. Large-scale and biophysically plausible BNN modeling requires solving multiple-terms, coupled and non-linear differential equations, making simulation computationally complex and memory intensive. This paper presents an adaptive simulation methodology in which neurons in the region of interest (ROI) follow high biological accurate models while the other neurons follow computation friendly models. To enable ROI based approximation, we propose a generic template based computing algorithm which unifies the data structure and computing flow for various neuron models. We implement the algorithms on CPU, GPU and embedded platforms, showing llx speedup with insignificant loss of biological details in the region of interest.