Connectivity Patterns in Neuronal Networks of Experimentally Defined Geometry

Abstract

Experimental control over the position and connectivity pattern of neurons on a surface is of central interest for applications in biotechnology, such as cell-based biosensors and tissue engineering. By restricting neuronal networks to a simple grid pattern, a drastic reduction of network complexity can be achieved relative to networks on homogeneous substrates. Therefore, patterned neuronal networks are also a valuable tool in research on neuronal signal transduction. Microcontact printing has emerged as a simple and efficient method for surface patterning to direct cellular attachment. Although the formation of synaptic contacts in networks of rat cortical cells on such surfaces has been demonstrated, evidence of more complex circuits has been lacking. Triple patch-clamp measurements were performed to analyze connectivity in neuronal networks complying with a gridshaped micropattern. Cells adhered stringently to the pattern and interconnected to a range of different types of circuits: linear connections, feedback loops, as well as branching and converging pathways. We conclude that in spite of the severe geometric restrictions, a complex repertoire of different connectivity patterns can form along the provided pathways. At the same time, network complexity is kept low enough to allow the study of these patterns at the resolution of single cell–cell contacts.