Functional Complexity of Engineered Neural Networks Self-Organized on Novel 3D Interfaces

Engineered neural networks are indispensable tools for studying neural function and dysfunction in controlled microenvironments. In vitro , neurons self-organize into complex assemblies with structural and functional similarities to in vivo circuits. Traditionally, these models are established on planar interfaces, but studies suggest that the lack of a three-dimensional growth space affects neuronal organization and function. While methods supporting 3D growth exist, reproducible 3D neuroengineering techniques compatible with electrophysiological recording methods are still needed. In this study, we developed a novel biocompatible interface made of the polymer SU-8 to support 3D network development. Using electron microscopy and immunocytochemistry, we show that neurons utilize these 3D scaffolds to self-assemble into complex, multi-layered networks. Furthermore, interfacing the scaffolds with custom microelectrode arrays enabled characterizing of electrophysiological activity. Both planar control networks and 3D networks displayed complex interactions with integrated and segregated functional dynamics. However, control networks showed stronger functional interconnections, higher entropy, and increased firing rates. In summary, our interfaces provide a versatile approach for supporting neural networks with a 3D growth environment, compatible with assorted electrophysiology and imaging techniques. This system can offer new insights into the impact of 3D topologies on neural network organization and function.