Data from: Fundamental activity constraints lead to specific interpretations of the connectome

The continuous integration of experimental data into coherent models of the brain is an increasing challenge of modern neuroscience. Such models provide a bridge between structure and activity, and identify the mechanisms giving rise to experimental observations. Nevertheless, structurally realistic network models of spiking neurons are necessarily underconstrained even if experimental data on brain connectivity are incorporated to the best of our knowledge. Guided by physiological observations, any model must therefore explore the parameter ranges within the uncertainty of the data. Based on simulation results alone, however, the mechanisms underlying stable and physiologically realistic activity often remain obscure. We here employ a mean-field reduction of the dynamics, which allows us to include activity constraints into the process of model construction. We shape the phase space of a multi-scale network model of the vision-related areas of macaque cortex by systematically refining its connectivity. Fundamental constraints on the activity, i.e., prohibiting quiescence and requiring global stability, prove sufficient to obtain realistic layer- and area-specific activity. Only small adaptations of the structure are required, showing that the network operates close to an instability. The procedure identifies components of the network critical to its collective dynamics and creates hypotheses for structural data and future experiments. The method can be applied to networks involving any neuron model with a known gain function.将实验数据持续整合为大脑的连贯模型，是现代神经科学日益严峻的挑战。此类模型搭建起结构与活动之间的桥梁，并可揭示催生实验观测结果的内在机制。尽管如此，即便基于现有已知的大脑连接实验数据构建具有结构真实性的脉冲神经元(spiking neurons)网络模型，该模型仍必然存在约束不足的问题。因此，任何模型都需在生理学观测的指导下，探索数据不确定性范围内的参数区间。然而，仅依靠仿真结果，往往难以明确稳定且符合生理学真实性的活动背后的作用机制。本文采用动力学的平均场约简(mean-field reduction)方法，可将活动约束纳入模型构建流程。我们通过系统性优化猕猴大脑视觉相关皮层脑区的多尺度网络模型的连接结构，塑造了该模型的相空间。研究发现，仅需施加两项基本活动约束——禁止模型陷入静息状态并要求全局稳定性，即可得到符合皮层层次与脑区特异性的真实活动模式，且仅需对网络结构进行小幅调整，表明该网络的运行状态接近失稳临界点。该方法可识别出对网络集体动力学至关重要的网络组件，并为结构数据研究与未来实验提供假说。本方法可推广应用于所有具备已知增益函数(gain function)的神经元模型网络。