Hasselmo Michael E, Giocomo Lisa M, Zilli Eric A
Center for Memory and Brain and Program in Neuroscience, Boston University, Boston, Massachusetts, USA.
Hippocampus. 2007;17(12):1252-71. doi: 10.1002/hipo.20374.
Intracellular recording and computational modelling suggest that interactions of subthreshold membrane potential oscillation frequency in different dendritic branches of entorhinal cortex stellate cells could underlie the functional coding of continuous dimensions of space and time. Among other things, these interactions could underlie properties of grid cell field spacing. The relationship between experimental data on membrane potential oscillation frequency (f) and grid cell field spacing (G) indicates a constant scaling factor H = fG. This constant scaling factor between temporal oscillation frequency and spatial periodicity provides a starting constraint that is used to derive the model of Burgess et al. (Hippocampus, 2007). This model provides a consistent quantitative link between single cell physiological properties and properties of spiking units in awake behaving animals. Further properties and predictions of this model about single cell and network physiological properties are analyzed. In particular, the model makes quantitative predictions about the change in membrane potential, single cell oscillation frequency, and network oscillation frequency associated with speed of movement, about the independence of single cell properties from network theta rhythm oscillations, and about the effect of variations in initial oscillatory phase on the pattern of grid cell firing fields. These same mechanisms of subthreshold oscillations may play a more general role in memory function, by providing a method for learning arbitrary time intervals in memory sequences.
细胞内记录和计算建模表明,内嗅皮层星状细胞不同树突分支中阈下膜电位振荡频率的相互作用可能是空间和时间连续维度功能编码的基础。除此之外,这些相互作用可能是网格细胞场间距特性的基础。膜电位振荡频率(f)与网格细胞场间距(G)的实验数据之间的关系表明存在一个恒定的比例因子H = fG。时间振荡频率与空间周期性之间的这个恒定比例因子提供了一个起始约束条件,用于推导Burgess等人(《海马体》,2007年)的模型。该模型在清醒行为动物的单细胞生理特性与尖峰单元特性之间提供了一致的定量联系。对该模型关于单细胞和网络生理特性的进一步特性及预测进行了分析。特别是,该模型对与运动速度相关的膜电位变化、单细胞振荡频率和网络振荡频率、单细胞特性相对于网络θ节律振荡的独立性以及初始振荡相位变化对网格细胞放电场模式的影响做出了定量预测。阈下振荡的这些相同机制可能通过提供一种在记忆序列中学习任意时间间隔的方法,在记忆功能中发挥更普遍的作用。