Division of Clinical and Computational Neuroscience, Krembil Brain Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada.
Department of Medicine (Neurology), University of Toronto, Toronto, ON, Canada.
Front Neural Circuits. 2021 Apr 21;15:643360. doi: 10.3389/fncir.2021.643360. eCollection 2021.
Computational models of neural circuits with varying levels of biophysical detail have been generated in pursuit of an underlying mechanism explaining the ubiquitous hippocampal theta rhythm. However, within the theta rhythm are at least two types with distinct frequencies associated with different behavioral states, an aspect that must be considered in pursuit of these mechanistic explanations. Here, using our previously developed excitatory-inhibitory network models that generate theta rhythms, we investigate the robustness of theta generation to intrinsic neuronal variability by building a database of heterogeneous excitatory cells and implementing them in our microcircuit model. We specifically investigate the impact of three key "building block" features of the excitatory cell model that underlie our model design: these cells' rheobase, their capacity for post-inhibitory rebound, and their spike-frequency adaptation. We show that theta rhythms at various frequencies can arise dependent upon the combination of these building block features, and we find that the speed of these oscillations are dependent upon the excitatory cells' response to inhibitory drive, as encapsulated by their phase response curves. Taken together, these findings support a hypothesis for theta frequency control that includes two aspects: (i) an internal mechanism that stems from the building block features of excitatory cell dynamics; (ii) an external mechanism that we describe as "inhibition-based tuning" of excitatory cell firing. We propose that these mechanisms control theta rhythm frequencies and underlie their robustness.
已经生成了具有不同生物物理细节水平的神经回路计算模型,以寻求解释普遍存在的海马体 theta 节律的潜在机制。然而,在 theta 节律中至少有两种具有不同频率的类型,这些类型与不同的行为状态相关,在寻求这些机制解释时必须考虑到这一点。在这里,我们使用之前开发的产生 theta 节律的兴奋性-抑制性网络模型,通过构建异质兴奋性细胞数据库并将其在我们的微电路模型中实现,来研究内在神经元变异性对 theta 产生的稳健性。我们特别研究了构成我们模型设计基础的兴奋性细胞模型的三个关键“构建块”特征对 theta 产生的影响:这些细胞的阈值、它们在后抑制反弹中的能力和它们的尖峰频率适应。我们表明,依赖于这些构建块特征的组合,可以产生各种频率的 theta 节律,并且我们发现这些振荡的速度取决于兴奋性细胞对抑制性驱动的反应,这由它们的相位响应曲线来描述。总之,这些发现支持了一种关于 theta 频率控制的假说,该假说包括两个方面:(i)源于兴奋性细胞动力学构建块特征的内在机制;(ii)我们描述为兴奋性细胞放电的“基于抑制的调谐”的外在机制。我们提出这些机制控制着 theta 节律的频率,并构成了它们的稳健性。