Directional intermodular coupling enriches functional complexity in biological neuronal networks

TL;DR

This study demonstrates that directional intermodular coupling in engineered neuronal networks reduces excessive synchrony and enhances functional complexity, combining bioengineering, modeling, and in vitro experiments to understand structure-function relationships.

Contribution

It introduces a novel microfluidic platform to reconstitute hierarchically modular neuronal networks with directional connections and links network topology to dynamics through theoretical modeling.

Findings

Directional connections suppress network synchrony

Modularity and directionality shape network dynamics

Dynamics can be predicted from eigendecomposition of transition matrices

Abstract

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.