The model parameters values.

The inquiry into the origin of brain complexity remains a pivotal question in neuroscience. While synaptic stimuli are acknowledged as significant, their efficacy often falls short in elucidating the extensive interconnections of the brain and nuanced levels of cognitive integration. Recent advances in neuroscience have brought the mechanisms underlying the generation of highly intricate dynamics, emergent patterns, and sophisticated oscillatory signals into question. Within this context, our study, in alignment with current research, postulates the hypothesis that ephaptic communication, in addition to synaptic mediation’s, may emerge as a prime candidate for unraveling optimal brain complexity. Ephaptic communication, hitherto little studied, refers to direct interactions of the electric field between adjacent neurons, without the mediation of traditional synapses (electrical or chemical). We propose that these electric field couplings may provide an additional layer of connectivity that facilitates the formation of complex patterns and emergent dynamics in the brain. In this investigation, we conducted a comparative analysis between two types of networks utilizing the Quadratic Integrate-and-Fire Ephaptic model (QIF-E): (I) a small-world synaptic network (ephaptic-off) and (II) a mixed composite network comprising a small-world synaptic network with the addition of an ephaptic network (ephaptic-on). Utilizing the Multiscale Entropy methodology, we conducted an in-depth analysis of the responses generated by both network configurations, with complexity assessed by integrating across all temporal scales. Our findings demonstrate that ephaptic coupling enhances complexity under specific topological conditions, considering variables such as time, spatial scales, and synaptic intensity. These results offer fresh insights into the dynamics of communication within the nervous system and underscore the fundamental role of ephapticity in regulating complex brain functions.对大脑复杂性起源的探究始终是神经科学领域的核心议题。尽管突触刺激已被证实具有重要作用，但其阐释大脑广泛互连性与精细认知整合层级的能力往往有所不足。近年来神经科学的进展，使得高度复杂动力学、涌现模式与复杂振荡信号产生背后的机制受到了重新审视。在此背景下，本研究结合当前研究前沿，提出假说：除突触介导作用外，电耦合通信（ephaptic communication）或可成为解析大脑最优复杂性的关键候选机制。电耦合通信（ephaptic communication）此前鲜有研究，指无需传统突触（电突触或化学突触）介导的相邻神经元间电场直接相互作用。我们提出，这类电场耦合可提供额外的连接维度，助力大脑复杂模式与涌现动力学的形成。本研究采用二次积分点火电耦合模型（Quadratic Integrate-and-Fire Ephaptic model, QIF-E），对两类网络开展对比分析：（I）小世界突触网络（ephaptic-off，即关闭电耦合模式）；（II）混合型复合网络，即在小世界突触网络基础上新增电耦合网络（ephaptic-on，即开启电耦合模式）。我们借助多尺度熵（Multiscale Entropy）方法，对两类网络构型产生的响应展开深入分析，通过整合所有时间尺度的信息评估系统复杂性。研究结果表明，在综合考量时间、空间尺度与突触强度等变量的特定拓扑条件下，电耦合可提升系统复杂性。上述发现为神经系统内通信动力学提供了全新视角，并凸显了电耦合在调控大脑复杂功能中的核心作用。