Towards a faithful stochastic model for brain digital twins

2026-09-12

AI Summary

研究提出了一种结合随机神经网络和马尔可夫调制泊松过程的新模型，以提高脑数字孪生在单个神经元和神经元群体尺度上的仿真保真度。

Abstract

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.

Problem

• brain digital twins
• neuronal mechanisms
• stochastic model
• fidelity

Innovation

• Random Neural Networks
• Markov-Modulated Poisson Processes
• Discrete Event System Specification