迈向大脑数字孪生的忠实随机模型

中文摘要 AI 辅助

大脑孪生意味着以保真度重现其电活动。这种活动源于多种跨尺度的神经机制，从单个神经元到整个脑区。当前的大型项目依赖于仅捕捉其中少数机制的模型。有些在单个神经元尺度上运作，另一些则在神经元群体尺度上运作。没有任何单一模型能以保真度同时捕捉这两个尺度。我们提出一种新的随机模型，基于随机神经网络与马尔可夫调制泊松过程的组合，该模型将提高大脑数字孪生的保真度。我们将该模型映射到离散事件系统规范。这产生了一个初始的可执行仿真模型，适合纳入大脑数字孪生。

英文摘要

Twinning the brain means reproducing its electrical activity with fidelity. This activity arises from many neuronal mechanisms across several scales, from a single neuron to whole brain regions. Current large initiatives rely on models that each capture only a few of these mechanisms. Some act at the scale of a single neuron, others at the scale of neuronal populations. No single model captures both scales with fidelity. We propose a new stochastic model, based on a combination of Random Neural Networks and Markov-Modulated Poisson Processes, that would improve the fidelity of a brain digital twin. We map the model to the Discrete Event System Specification. This yields an initial executable simulation model suitable for incorporation into a brain digital twin.

发表机构

• DIRO, Université de Montréal（蒙特利尔大学）

机构由 AI 辅助整理，请以论文原文为准。