  '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.
