In this article, I discuss the concept of robustness in neuroscience. Various mechanisms for making systems robust have been discussed across biology and neuroscience (e.g., redundancy and fail-safes). Many of these notions originate from engineering. I argue that concepts borrowed from engineering aid neuroscientists in (1) operationalizing robustness, (2) formulating hypotheses about mechanisms for robustness, and (3) quantifying robustness. Furthermore, I argue that the significant disanalogies between brains and engineered artifacts raise important questions about the applicability of the engineering framework. I argue that the use of such concepts should be understood as a kind of simplifying idealization.