核心要点
- 问题/背景
- 这篇 Nature Communications 论文把神经动力学研究从短实验任务扩展到一周真实自由行为：20 名被试同时记录颅内多电极和视频行为。
- 方法/机制
- 作者用动力学深度学习算法发现神经动态与昼夜节律、心率、社交、看屏幕、睡眠深度等现实行为变量相关。
- 结果/证据
- 关键机制是行为转换时出现快速、混沌式神经探索，随后稳定到新状态；大尺度动态围绕与意识和神经生理状态相关的 center manifold / attractor 组织。
- 收录价值
- 它值得收录，因为它给出了真实世界认知中“稳定状态 + 混沌转换”的神经动力学图景，对持续运行 agent 的状态转移、睡眠/疲劳扰动、稳定吸引子和探索-稳定平衡建模有概念价值。
原始摘要与中文对照
中文对照翻译
人脑一周生活揭示了由混沌状转变打断的稳定状态。关键的真实世界神经认知过程，例如进入对话和神经生理波动，在真实世界环境中以分钟到天为单位变化。我们利用了二十名受试者在一周无约束、自发行为期间的同步多电极颅内和视频记录。利用动态深度学习算法，我们发现了与昼夜节律、心率以及行为的多个方面（社交、看屏幕、睡眠深度等）相关的神经动力学。行为之间的转换与快速、混沌的神经探索爆发相关，这些探索稳定为新的状态。尽管存在这种混沌，大规模动力学仍锚定在一个与神经生理和意识状态相关的稳定中心流形上，其中一个中心吸引子涉及默认模式网络的激活。当受到睡眠剥夺扰动时，神经转换更加混沌，并且中心吸引子周围的动力学受到抑制，这表明由于睡眠不足导致神经动力学控制减弱。这些发现强调了大脑如何在稳定平衡点周围进行混沌转换，以在真实世界行为中平衡动态探索和稳定平衡。
原始摘要
Critical real-world neurocognitive processes, such as settling into a conversation and neurophysiological fluctuations, vary over minutes-to-days in real-world environments. We harnessed simultaneous multi-electrode intracranial and video recordings in twenty people during a week of unconstrained, spontaneous behavior. Using dynamical deep learning algorithms, we found neurodynamics linked to circadian rhythm, heart rate, and multiple aspects of behavior (socializing, watching a screen, sleep depth, etc.). Transitioning between behaviors was associated with bursts of rapid, chaotic neural exploration that stabilized into new states. Despite this chaos, large-scale dynamics anchored to a stabilizing center manifold associated with neurophysiological and conscious states, with a central attractor involving default mode network activation. When perturbed by sleep deprivation, neural transitions were more chaotic and dynamics around the central attractor were suppressed, suggesting diminished neurodynamic control due to lack of sleep. These findings highlight how the brain chaotically transitions around a stabilizing equilibrium to balance dynamic exploration and stable equilibria during real-world behavior.