Department of Numerical Analysis and Scientific Computing, Simula Research Laboratory, Oslo, Norway.
Department of Mathematics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
PLoS Comput Biol. 2024 Nov 12;20(11):e1012114. doi: 10.1371/journal.pcbi.1012114. eCollection 2024 Nov.
Most computational models of neurons assume constant ion concentrations, disregarding the effects of changing ion concentrations on neuronal activity. Among the models that do incorporate ion concentration dynamics, simplifications are often made that sacrifice biophysical consistency, such as neglecting the effects of ionic diffusion on electrical potentials or the effects of electric drift on ion concentrations. A subset of models with ion concentration dynamics, often referred to as electrodiffusive models, account for ion concentration dynamics in a way that ensures a biophysical consistent relationship between ion concentrations, electric charge, and electrical potentials. These models include compartmental single-cell models, geometrically explicit models, and domain-type models, but none that model neuronal network dynamics. To address this gap, we present an electrodiffusive network model with multicompartmental neurons and synaptic connections, which we believe is the first compartmentalized network model to account for intra- and extracellular ion concentration dynamics in a biophysically consistent way. The model comprises an arbitrary number of "units," each divided into three domains representing a neuron, glia, and extracellular space. Each domain is further subdivided into a somatic and dendritic layer. Unlike conventional models which focus primarily on neuronal spiking patterns, our model predicts intra- and extracellular ion concentrations (Na+, K+, Cl-, and Ca2+), electrical potentials, and volume fractions. A unique feature of the model is that it captures ephaptic effects, both electric and ionic. In this paper, we show how this leads to interesting behavior in the network. First, we demonstrate how changing ion concentrations can affect the synaptic strengths. Then, we show how ionic ephaptic coupling can lead to spontaneous firing in neurons that do not receive any synaptic or external input. Lastly, we explore the effects of having glia in the network and demonstrate how a strongly coupled glial syncytium can prevent neuronal depolarization blocks.
大多数神经元的计算模型都假设离子浓度是恒定的，而忽略了离子浓度变化对神经元活动的影响。在那些确实包含离子浓度动力学的模型中，通常会做出牺牲生物物理一致性的简化，例如忽略离子扩散对电势能的影响或电漂移对离子浓度的影响。一类具有离子浓度动力学的模型，通常称为电扩散模型，以一种确保离子浓度、电荷量和电势能之间具有生物物理一致性关系的方式来解释离子浓度动力学。这些模型包括有隔间的单细胞模型、几何显式模型和域类型模型，但没有一个模型可以模拟神经元网络动力学。为了解决这一差距，我们提出了一种具有多隔间神经元和突触连接的电扩散网络模型，我们相信这是第一个以生物物理一致的方式解释细胞内和细胞外离子浓度动力学的隔间化网络模型。该模型由任意数量的“单元”组成，每个单元分为代表神经元、神经胶质和细胞外空间的三个域。每个域进一步细分为一个体和树突层。与主要关注神经元尖峰模式的传统模型不同，我们的模型预测细胞内和细胞外离子浓度（Na+、K+、Cl-和 Ca2+）、电势能和体积分数。该模型的一个独特特征是它捕获了电突触和离子突触效应。在本文中，我们展示了这如何导致网络中的有趣行为。首先，我们展示了离子浓度如何影响突触强度。然后，我们展示了离子电突触耦合如何导致没有接收任何突触或外部输入的神经元自发放电。最后，我们探索了网络中存在神经胶质的影响，并展示了强耦合的神经胶质合胞体如何防止神经元去极化阻断。
