GANGLIA: A Tool for Designing Customized Neuron Circuit Patterns.

Current biological neural controllers in biohybrid robotics rely on networks of self-assembled neurons. However, to be able to reproducibly create neuron circuits optimized for specific functions, the connections that the neurons form need to be controlled. Towards addressing this need, we have developed a tool for the Generation of Automatic Neuron Graph-Like Interconnected Arrangements (GANGLIA), which automatically generates micro-patterns using graph drawing algorithms to place the cells based on an input array of neuron connections and generate micro-patterns in a variety of common file formats. Four network connectivities, ranging in levels of complexity, were used to assess GANGLIA’s performance. As the complexity increased, the number of intersections of neurite paths and the amount of time GANGLIA took to generate the pattern increased. However, for the most complex circuit tested here, GANGLIA took less than 8 s to generate a micro-pattern, which is faster than manually generating an equivalent model. The fast, automatic generation of micro-patterns has the potential to support the design and fabrication process of complex neuron circuits in vitro for biohybrid control.