Abstract Today, based on brain imaging analyses, we can consider the brilliant metaphor about event discreteness of the conscious process by William James (1890) to be an experimental fact. Such events compose sequences: linguistic, episodic memory, motor behavior, etc., whose dynamics are robust, reproducible, and sensitively react to incoming informational signals. The human mind is able to process, understand and predict time-dependent information about the environment and about ourselves, and generate corresponding commands to control behavior. Many experiments have indicated that the mind relies on sequential dynamics to carry out these tasks. Based on brain imaging experiments, we discuss here a set of key principles and their instantiation in nonlinear differential equations to form a dynamical theory of consciousness and creativity. General hierarchical models of consciousness and creativity include coupled low-dimensional equations that govern cooperative variables for several cognitive modalities: episodic (semantic) memory, working memory, attention, emotion, perception and their sequential interaction. In the phase spaces spanned by variables of these models, the joint transient dynamics of cognitive modalities is represented by coupled heteroclinic networks which share complex metastable states. The interaction of such states is responsible for the robustness of transient neural dynamics involved in the generation of thoughts and in the programming of behavior. In the framework of the analyzed dynamical models, we discuss the interaction of cognitive processes and the generation of new information in creativity.

中文翻译：

---

复杂网络的顺序动力学：意识和创造力

摘要 今天，基于脑成像分析，我们可以将威廉詹姆斯（1890）关于意识过程的事件离散性的精彩比喻视为一个实验事实。此类事件构成序列：语言、情景记忆、运动行为等，其动态是稳健的、可重复的，并对传入的信息信号做出敏感反应。人类的大脑能够处理、理解和预测关于环境和关于我们自己的时间相关信息，并生成相应的命令来控制行为。许多实验表明，大脑依靠顺序动力学来执行这些任务。基于脑成像实验，我们在这里讨论了一组关键原理及其在非线性微分方程中的实例化，以形成意识和创造力的动力学理论。意识和创造力的一般分层模型包括耦合的低维方程，这些方程控制几种认知模式的合作变量：情景（语义）记忆、工作记忆、注意力、情感、感知及其顺序交互。在这些模型的变量跨越的相空间中，认知模式的联合瞬态动力学由共享复杂亚稳态的耦合异宿网络表示。这种状态的相互作用是产生思想和行为编程中涉及的瞬态神经动力学的鲁棒性的原因。在分析的动态模型的框架内，我们讨论了认知过程的相互作用和创造力中新信息的产生。