Engineering modular circuit architectures of in vitro neuronal networks

The brain’s inherent modular architecture underpins its remarkable functional capacity, robustness, and ability to efficiently integrate and segregate information. A rapidly expanding research field is exploring how this modularity can be replicated in in vitro grown neuronal cell cultures to gain deeper insights into the mechanisms governing efficient information transmission in biological neuronal networks. One of the aspirations is to leverage these biological mechanisms to improve the efficiency and generalizability of artificial learning systems.

High-density microelectrode array platforms are powerful tools to study these in vitro assembled neuronal cell cultures at single cell level. MEA chips can record and stimulate the neuronal circuits grown on top of the sub-cellular sized electrodes by recording their electrical signatures or stimulating them. However, the structure of these circuits often exhibits large variability which complicates consistent recording of functional network features.

In this PhD project you will explore bottom-up approaches for engineering neuronal circuits to get more reproducible electrogenic activity and connectivity patterns by recapitulating the modular structure of brain circuits. For this work you can rely on available toolboxes within imec to constrain and guide neuronal growth. Novel experimental paradigms will be explored to infer network connectivity patterns that are based on external stimulation with different modalities (e.g. mild voltage stimulation or optogenics). You will use biophysical modeling to extract mechanistic parameters from recorded electrophysiology data and to unlock new biological insights.

For this interdisciplinary project, you should have a background in biomedical engineering, engineering, neuroscience, biomedical science or related fields. Experience in electrophysiology would be desirable. Programming experience, primarily Python, Matlab is considered a strong asset. You will be supervised and supported by a team of data/computer scientists, engineers and biologists at imec.