用近似贝叶斯计算校准基于代理的随机神经元生长模型。

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2024-10-08 DOI:10.1007/s00285-024-02144-2
Tobias Duswald, Lukas Breitwieser, Thomas Thorne, Barbara Wohlmuth, Roman Bauer
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引用次数: 0

摘要

了解基因编码的规则如何驱动和引导复杂的神经元生长过程对于理解大脑的结构至关重要,而基于代理的模型(ABM)提供了一种强大的模拟方法来进一步加深这种理解。然而,准确校准这些模型仍然是一项挑战。在这里,我们提出了近似贝叶斯计算(ABC)的新应用来解决这个问题。近似贝叶斯计算基于参数化的随机规则,这些规则描述了系统的小组成部分(即所谓的代理)的时间演化,导致需要适当处理的随机模拟。在数学上,校准定义了一个随机逆问题。我们建议使用 ABC 在贝叶斯环境中解决这个问题。我们通过所谓的形态计量学量化单个神经元的形态信息,并利用统计距离来测量其群体之间的差异,从而促进数据与模拟之间的重复比较。我们对合成数据和实验数据进行了实验。我们发现,利用序列蒙特卡洛采样和瓦瑟斯坦距离的 ABC 算法可以为具有代表性的 ABM 找到准确的后验参数分布。我们进一步证明,这些 ABM 捕捉到了海马(CA1)锥体细胞的特定特征。总之,这项工作为校准基于代理的神经元生长模型建立了一个稳健的框架,并为未来使用贝叶斯技术进行模型构建、验证和适当性评估的研究打开了大门。
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Calibration of stochastic, agent-based neuron growth models with approximate Bayesian computation.

Understanding how genetically encoded rules drive and guide complex neuronal growth processes is essential to comprehending the brain's architecture, and agent-based models (ABMs) offer a powerful simulation approach to further develop this understanding. However, accurately calibrating these models remains a challenge. Here, we present a novel application of Approximate Bayesian Computation (ABC) to address this issue. ABMs are based on parametrized stochastic rules that describe the time evolution of small components-the so-called agents-discretizing the system, leading to stochastic simulations that require appropriate treatment. Mathematically, the calibration defines a stochastic inverse problem. We propose to address it in a Bayesian setting using ABC. We facilitate the repeated comparison between data and simulations by quantifying the morphological information of single neurons with so-called morphometrics and resort to statistical distances to measure discrepancies between populations thereof. We conduct experiments on synthetic as well as experimental data. We find that ABC utilizing Sequential Monte Carlo sampling and the Wasserstein distance finds accurate posterior parameter distributions for representative ABMs. We further demonstrate that these ABMs capture specific features of pyramidal cells of the hippocampus (CA1). Overall, this work establishes a robust framework for calibrating agent-based neuronal growth models and opens the door for future investigations using Bayesian techniques for model building, verification, and adequacy assessment.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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