It is well known that neuronal networks are capable of transmitting complex spatiotemporal information in the form of precise sequences of neuronal discharges characterized by recurrent patterns. At the same time, the synchronized activity of large ensembles produces local field potentials that propagate through highly dynamic oscillatory waves, such that, at the whole brain scale, complex spatiotemporal dynamics of electroencephalographic (EEG) signals may be associated to sensorimotor decision making processes. Despite these experimental evidences, the link between highly temporally organized input patterns and EEG waves has not been studied in detail. Here, we use a neural mass model to investigate to what extent precise temporal information, carried by deterministic nonlinear attractor mappings, is filtered and transformed into fluctuations in phase, frequency and amplitude of oscillatory brain activity. The phase shift that we observe, when we drive the neural mass model with specific chaotic inputs, shows that the local field potential amplitude peak appears in less than one full cycle, thus allowing traveling waves to encode temporal information. After converting phase and amplitude changes obtained into point processes, we quantify input–output similarity following a threshold-filtering algorithm onto the amplitude wave peaks. Our analysis shows that the neural mass model has the capacity for gating the input signal and propagate selected temporal features of that signal. Finally, we discuss the effect of local excitatory/inhibitory balance on these results and how excitability in cortical columns, controlled by neuromodulatory innervation of the cerebral cortex, may contribute to set a fine tuning and gating of the information fed to the cortex.

中文翻译：

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通过皮质柱的神经质量模型处理复杂的时间模式。

众所周知，神经元网络能够以特征为递归模式的神经元放电的精确序列的形式传输复杂的时空信息。同时，大型集合体的同步活动会产生通过高动态振荡波传播的局部场电势，因此在整个大脑范围内，脑电图（EEG）信号的复杂时空动态可能与感觉运动决策过程相关。尽管有这些实验证据，但尚未详细研究高度时间性组织的输入模式与EEG波之间的联系。在这里，我们使用神经质量模型来调查确定性非线性吸引子映射所承载的精确时间信息的程度，被过滤并转换为振荡脑活动的相位，频率和幅度的波动。当我们使用特定的混沌输入驱动神经质量模型时，我们观察到的相移表明局部场电势振幅峰值出现在不到一个完整的周期内，因此允许行波对时间信息进行编码。将获得的相位和幅度变化转换为点过程后，我们将根据阈值滤波算法将输入输出相似度量化到幅度波峰上。我们的分析表明，神经质量模型具有门控输入信号并传播该信号的选定时间特征的能力。最后，我们讨论了局部兴奋/抑制平衡对这些结果的影响，以及皮质柱的兴奋性如何，