  Hierarchically modular organization is a canonical network topology that is
evolutionarily conserved in the nervous systems of animals. Within the network,
neurons form directional connections defined by the growth of their axonal
terminals. However, this topology is dissimilar to the network formed by
dissociated neurons in culture because they form randomly connected networks on
homogeneous substrates. In this study, we fabricated microfluidic devices to
reconstitute hierarchically modular neuronal networks in culture (in vitro) and
investigated how non-random structures, such as directional connectivity
between modules, affect global network dynamics. Embedding directional
connections in a pseudo-feedforward manner suppressed excessive synchrony in
cultured neuronal networks and enhanced the integration-segregation balance.
Modeling the behavior of biological neuronal networks using spiking neural
networks (SNNs) further revealed that modularity and directionality cooperate
to shape such network dynamics. Finally, we demonstrate that for a given
network topology, the statistics of network dynamics, such as global network
activation, correlation coefficient, and functional complexity, can be
analytically predicted based on eigendecomposition of the transition matrix in
the state-transition model. Hence, the integration of bioengineering and cell
culture technologies enables us not only to reconstitute complex network
circuitry in the nervous system but also to understand the structure-function
relationships in biological neuronal networks by bridging theoretical modeling
with in vitro experiments.
