背侧(DRN)和中间(MRN)是参与类似功能的重要细胞核,包括情绪和睡眠,但扮演不同的角色。这些细胞核具有不同的神经元类型和一组神经元连接的组成,除其他因素外,确定它们的神经元动力学。大多数作品使用经典措施描述神经元动力学,例如使用平均尖峰频率(FR),变异系数(CV),和动作电位持续时间(APD)。在目前的研究中,为了完善神经元放电曲线的特征,我们检查了中缝核内的神经元。通过利用非线性措施,我们的目标是辨别这些措施的冗余和互补性,特别是与经典方法相比。要做到这一点,我们使用刺间间隔的Shannon熵(Bins熵)分析了氨基甲酸乙酯麻醉大鼠的两个核中的神经元基底放电曲线,序数模式的排列熵(OP熵),和置换Lempel-Ziv复杂度(PLZC)。首先,我们发现经典(即,FR,CV,和APD)和非线性测量无法区分DRN和MRN神经元的动力学,除了OP熵。我们还发现措施之间有很强的关系,包括带有FR的CV,带箱熵的CV,和FR与PLZC,这意味着冗余信息。然而,APD和OP熵与其他测试的度量有微弱或没有关系,这表明它们为神经元放电曲线的表征提供了补充信息。其次,我们研究了这些措施如何受到发射模式的振荡特性的影响,包括节奏,爆裂模式,和时钟一样的行为。我们发现所有的措施都对节奏敏感,除了OP熵。总的来说,我们的工作强调OP熵是表征神经元放电模式的强大而有用的量。
The dorsal (DRN) and median (MRN) raphe are important nuclei involved in similar functions, including mood and sleep, but playing distinct roles. These nuclei have a different composition of neuronal types and set of neuronal connections, which among other factors, determine their neuronal dynamics. Most works characterize the neuronal dynamics using classic measures, such as using the average spiking frequency (FR), the coefficient of variation (CV), and action potential duration (APD). In the current study, to refine the characterization of neuronal firing profiles, we examined the neurons within the raphe nuclei. Through the utilization of nonlinear measures, our objective was to discern the redundancy and complementarity of these measures, particularly in comparison with classic methods. To do this, we analyzed the neuronal basal firing profile in both nuclei of urethane-anesthetized rats using the Shannon entropy (Bins Entropy) of the inter-spike intervals, permutation entropy of ordinal patterns (OP Entropy), and Permutation Lempel-Ziv Complexity (PLZC). Firstly, we found that classic (i.e., FR, CV, and APD) and nonlinear measures fail to distinguish between the dynamics of DRN and MRN neurons, except for the OP Entropy. We also found strong relationships between measures, including the CV with FR, CV with Bins entropy, and FR with PLZC, which imply redundant information. However, APD and OP Entropy have either a weak or no relationship with the rest of the measures tested, suggesting that they provide complementary information to the characterization of the neuronal firing profiles. Secondly, we studied how these measures are affected by the oscillatory properties of the firing patterns, including rhythmicity, bursting patterns, and clock-like behavior. We found that all measures are sensitive to rhythmicity, except for the OP Entropy. Overall, our work highlights OP Entropy as a powerful and useful quantity for the characterization of neuronal discharge patterns.