Using Multi-Compartment Ensemble Modeling As an Investigative Tool of Spatially Distributed Biophysical Balances: Application to Hippocampal Oriens-Lacunosum/Moleculare (O-LM) Cells

Multi-compartmental models of neurons provide insight into the complex, integrative properties of dendrites. Because it is not feasible to experimentally determine the exact density and kinetics of each channel type in every neuronal compartment, an essential goal in developing models is to help characterize these properties. To address biological variability inherent in a given neuronal type, there has been a shift away from using hand-tuned models towards using ensembles or populations of models. In collectively capturing a neuron's output, ensemble modeling approaches uncover important conductance balances that control neuronal dynamics. However, conductances are never entirely known for a given neuron class in terms of its types, densities, kinetics and distributions. Thus, any multi-compartment model will always be incomplete. In this work, our main goal is to use ensemble modeling as an investigative tool of a neuron's biophysical balances, where the cycling between experiment and model is a design criterion from the start. We consider oriens-lacunosum/moleculare (O-LM) interneurons, a prominent interneuron subtype that plays an essential gating role of information flow in hippocampus. O-LM cells express the hyperpolarization-activated current (Ih). Although dendritic Ih could have a major influence on the integrative properties of O-LM cells, the compartmental distribution of Ih on O-LM dendrites is not known. Using a high-performance computing cluster, we generated a database of models that included those with or without dendritic Ih. A range of conductance values for nine different conductance types were used, and different morphologies explored. Models were quantified and ranked based on minimal error compared to a dataset of O-LM cell electrophysiological properties. Co-regulatory balances between conductances were revealed, two of which were dependent on the presence of dendritic Ih. These findings inform future experiments that differentiate between somatic and dendritic Ih, thereby continuing a cycle between model and experiment.

神经元多室模型（multi-compartmental model）可助力研究者深入理解树突复杂的整合特性。由于通过实验测定每个神经元室中每种通道类型的确切密度与动力学特性并不现实，开发模型的核心目标之一便是助力表征这些特性。为应对特定神经元类型固有的生物学变异性，研究范式已从使用手动校准模型转向采用模型集成（ensemble）或模型种群。在协同拟合神经元输出的过程中，模型集成建模方法可揭示调控神经元动力学的关键电导平衡机制。然而，就特定神经元类别的通道类型、密度、动力学特性及分布而言，其电导特性从未被完全探明。因此，任何多室模型都始终存在不完备之处。本研究的核心目标是将模型集成建模作为探究神经元生物物理平衡机制的工具，并从一开始就将实验与模型的循环迭代作为设计准则。本研究聚焦于oriens-lacunosum/moleculare（O-LM）中间神经元——这是一类重要的中间神经元亚型，在海马体的信息流中发挥关键的门控作用。O-LM神经元表达超极化激活电流（Ih）。尽管树突状Ih可能对O-LM神经元的整合特性产生重大影响，但目前尚不清楚Ih在O-LM神经元树突上的室间分布情况。本研究借助高性能计算集群，构建了包含带有树突状Ih和不带树突状Ih的模型数据库。研究中为9种不同的电导类型设置了一系列电导值，并探索了不同的神经元形态。研究人员基于O-LM神经元电生理特性数据集，以误差最小化为标准对模型进行量化与排序。研究揭示了电导之间的协同调控平衡机制，其中两种平衡机制依赖于树突状Ih的存在。上述研究结果可为后续区分体细胞与树突状Ih的实验提供指导，进而延续实验与模型的循环迭代研究范式。