'If I cannot build it, I do not understand it.' So said Nobel laureate Richard Feynman, and by his metric, we understand a bit about physics, less about chemistry, and almost nothing about biology. When we fully understand a phenomenon, we can specify its entire sequence of events, causes, and effects so completely that it is possible to fully simulate it, with all its internal mechanisms intact. Achieving that level of understanding is rare. It is commensurate with constructing a full design for a machine that could serve as a stand-in for the thing being studied. To understand a phenomenon sufficiently to fully simulate it is to understand it computationally. 'Computation' does not refer to computers per se. Rather, it refers to the underlying principles and methods that make them work. As Turing Award recipient Edsger Dijkstra said, computational science 'is no more about computers than astronomy is about telescopes.' Computational science is the study of the hidden rules underlying complex phenomena from physics to psychology. Computational neuroscience, then, has the aim of understanding brains sufficiently well to be able to simulate their functions, thereby subsuming the twin goals of science and engineering: deeply understanding the inner workings of our brains, and being able to construct simulacra of them. As simple robots today substitute for human physical abilities, in settings from factories to hospitals, so brain engineering will construct stand-ins for our mental abilities, and possibly even enable us to fix our brains when they break.