Department of Physics and Astronomy, University of California, Los Angeles, Los Angeles, CA, USA.
HRL Laboratories, Malibu, CA, USA.
Nat Commun. 2024 May 8;15(1):3542. doi: 10.1038/s41467-024-47617-6.
Understanding the functional connectivity between brain regions and its emergent dynamics is a central challenge. Here we present a theory-experiment hybrid approach involving iteration between a minimal computational model and in vivo electrophysiological measurements. Our model not only predicted spontaneous persistent activity (SPA) during Up-Down-State oscillations, but also inactivity (SPI), which has never been reported. These were confirmed in vivo in the membrane potential of neurons, especially from layer 3 of the medial and lateral entorhinal cortices. The data was then used to constrain two free parameters, yielding a unique, experimentally determined model for each neuron. Analytic and computational analysis of the model generated a dozen quantitative predictions about network dynamics, which were all confirmed in vivo to high accuracy. Our technique predicted functional connectivity; e. g. the recurrent excitation is stronger in the medial than lateral entorhinal cortex. This too was confirmed with connectomics data. This technique uncovers how differential cortico-entorhinal dialogue generates SPA and SPI, which could form an energetically efficient working-memory substrate and influence the consolidation of memories during sleep. More broadly, our procedure can reveal the functional connectivity of large networks and a theory of their emergent dynamics.
理解大脑区域之间的功能连接及其涌现的动力学是一个核心挑战。在这里，我们提出了一种理论-实验混合方法，涉及最小计算模型和体内电生理测量之间的迭代。我们的模型不仅预测了在 Up-Down-State 振荡期间的自发持续活动（SPA），还预测了从未报道过的不活动（SPI）。这些在神经元的膜电位中得到了体内证实，特别是在中间和外侧内嗅皮层的第 3 层。然后，将数据用于约束两个自由参数，从而为每个神经元生成一个独特的、经过实验确定的模型。对模型的分析和计算分析生成了十几个关于网络动力学的定量预测，这些预测在体内都得到了高精度的证实。我们的技术预测了功能连接；例如，内侧内嗅皮层的递归兴奋比外侧内嗅皮层更强。这也与连接组学数据一致。这项技术揭示了差异皮质-内嗅对话如何产生 SPA 和 SPI，这可能形成一个能量有效的工作记忆基质，并影响睡眠期间记忆的巩固。更广泛地说，我们的程序可以揭示大型网络的功能连接及其涌现的动力学理论。
