我们提出了一种用于高维脑体状态的计算框架，作为由内部感觉控制的嵌套内部和外部动力学的瞬态实施例。统一最近的理论工作，我们提出了将任意状态复杂性降低到可观察到的特征数量的方法，以便准确预测和干预病理轨迹。
We propose a computational framework for high-dimensional brain-body states as transient embodiments of nested internal and external dynamics governed by interoception. Unifying recent theoretical work, we suggest ways to reduce arbitrary state complexity to an observable number of features in order to accurately predict and intervene in pathological trajectories.
