On the Robustness of the Drosophila Neural Network

The reliability of a network is often described by its robustness under errors or attacks. The robustness of many complex networks has been understood from their scale-free structure. Indeed, scale-free networks are typically resilient to random failures, while vulnerable to targeted attacks. Nevertheless, the progress of building a robust network in the development of a biological neural network has not been studied because of the lack of proper data. Here, we examined the robustness of a simplified neural circuitry built from about ten thousand single neurons of the Drosophila brain; we found the network is resilient under both errors and attacks. Furthermore, we observe how such a resiliency is associated with the accumulation of neurons along their birth stages. Being able to understand and replicate the conditions behind such a robust structure would have a great impact in designing reliable networks in different application domains.