  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, posits the hypothesis that ephaptic communication may emerge as the
primary candidate for unraveling optimal brain complexity. 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.
