发表机构
School of Physics, The University of Sydney; ARC Centre of Excellence for Integrative Brain Function, The University of Sydney（悉尼大学物理学院; 悉尼大学整合脑功能卓越研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究结合小鼠视觉皮层记录与电路建模，提出自适应分数态，揭示视觉层级中动力学指数变化源于抑制减弱，实现快速响应与长期整合的共存。
AI 中文摘要
皮层回路必须在整合过去信息的同时灵活响应新输入，然而神经活动如何调和这些相互竞争的需求仍不清楚。通过将来自六个小鼠视觉区域的Neuropixels记录与机制性电路建模相结合，我们识别出一种动力学状态，在该状态中，重尾超扩散波动与长程时间依赖性和振荡共存。我们使用有效的平均场理论将该状态形式化为自适应分数（AF）态，其中空间和时间分数阶导数分别捕捉重尾偏移和长程记忆。我们发现，表征AF态的动力学指数在视觉层级中系统性变化：较高级视觉区域表现出较弱的超扩散和较强的时间记忆。在电路模型中，这种层级性转变源于有效抑制的逐渐减弱。这些发现将经典的时间尺度层级扩展为动力学状态的层级，并表明AF态允许皮层区域共同表达快速响应和长期时间整合。
英文摘要
Cortical circuits must respond flexibly to new inputs while integrating information about the past, yet the way in which neural activity reconciles these competing demands remains unclear. Combining Neuropixels recordings from six mouse visual areas with mechanistic circuit modeling, we identify a dynamical regime in which heavy-tailed superdiffusive fluctuations coexist with long-range temporal dependence and oscillations. We formalize this regime as the adaptive fractional (AF) state, using an effective mean-field theory in which spatial and temporal fractional derivatives capture heavy-tailed excursions and long-range memory, respectively. We find that dynamical exponents characterizing the AF state vary systematically across the visual hierarchy: higher visual areas exhibit weaker superdiffusion and stronger temporal memory. In the circuit model, this hierarchical shift emerges from a progressive weakening of effective inhibition. These findings extend the classical hierarchy of timescales to a hierarchy of dynamical regimes and suggest that the AF state allows cortical areas to jointly express rapid responses and long-term temporal integration.
Comments47 pages (41 main + 6 supplementary), 4 figures, 1 table, 4 supplementary figures, 1 supplementary video (ancillary file). Code: https://github.com/brendanjohnharris/WorkingRegime.jl
