A Bottom-up Approach for the Generation of Neural Circuits

The complexity of neural circuits represents a challenging obstacle for the mapping, manipulation and replication of biological neural networks to study information processing related to nerve function. This Thesis presents a bottom-up workflow to generate in vitro arrays with a defined structure by the controlled individual manipulation and positioning of neurons from cultures. Combined with the adaptation of an open-source automated workstation to improve precision, efficiency and reproducibility, the developed automated process provided a consistent and accessible approach to reproduce specific architectures of simple neural networks as biological models for high-throughput experiments and the study of neural function.