Circadian rhythmicity lies at the center of various important physiological and behavioral processes in mammals, such as sleep, metabolism, homeostasis, mood changes and more. It has been shown that this rhythm arises from self-sustained biomolecular oscillations of a neuronal network located in the Suprachiasmatic Nucleus (SCN). Under normal circumstances, this network remains synchronized to the day-night cycle due to signaling from the retina. Misalignment of these neuronal oscillations with the external light signal can disrupt numerous physiological functions and take a long-lasting toll on health and well-being. In this work, we study a modern computational neuroscience model to determine the limits of circadian synchronization to external light signals of different frequency and duty cycle. We employ a matrix-free approach to locate periodic steady states of the high-dimensional model for various driving conditions. Our algorithmic pipeline enables numerical continuation and construction of bifurcation diagrams w.r.t. forcing parameters. We computationally explore the effect of heterogeneity in the circadian neuronal network, as well as the effect of corrective therapeutic interventions, such as that of the drug molecule Longdaysin. Lastly, we employ unsupervised learning to construct a data-driven embedding space for representing neuronal heterogeneity.
翻译:在正常情况下,由于从视网膜发出信号,这个网络仍然与日间循环同步。这些神经神经振动与外部光信号的不协调会破坏许多生理功能,并对健康和福祉造成长期的伤害。在这项工作中,我们研究现代计算神经科学模型,以确定与不同频率和工作周期的外部光信号同步的临界点。我们采用无基方法,为各种驾驶条件定出高度模型的定期稳定状态。我们的算法管道使得数字延续和构建双向图 w.r.t. 强制参数。我们用计算方式探索了长期性神经神经神经科学模型的遗传性影响,作为长期性内压性神经内分泌神经内分泌的模型,作为长期性神经内分泌神经内分泌的模型,作为长期性神经内分泌内分泌的模型,作为长期性神经内分泌性内分泌的模型,作为长期性神经内分泌神经内分泌的模型,作为最后的内向性数据,作为长期性神经内存的内流。