论文标题
基于模型的机器学习关键大脑动态
Model-based machine learning of critical brain dynamics
论文作者
论文摘要
在某些大脑活动模型中可以准确地证明关键性，但是在经验数据中识别危机仍然具有挑战性。我们培训了一个完全连接的深神经网络，以学习在人脑解剖结构上展开的令人兴奋的模型的阶段。然后将该网络应用于从觉醒到深度睡眠的下降期间获得的脑范围范围FMRI数据。我们报告了与临界点的预测接近度与集群大小分布的指数之间的高度相关性，这表明了亚临界动力学。该结果表明，可以利用概念模型来确定真实神经系统的动态状态。
Criticality can be exactly demonstrated in certain models of brain activity, yet it remains challenging to identify in empirical data. We trained a fully connected deep neural network to learn the phases of an excitable model unfolding on the anatomical connectome of human brain. This network was then applied to brain-wide fMRI data acquired during the descent from wakefulness to deep sleep. We report high correlation between the predicted proximity to the critical point and the exponents of cluster size distributions, indicative of subcritical dynamics. This result demonstrates that conceptual models can be leveraged to identify the dynamical regime of real neural systems.
