  Although the brain has long been considered a potential inspiration for
future computing, Moore's Law - the scaling property that has seen revolutions
in technologies ranging from supercomputers to smart phones - has largely been
driven by advances in materials science. As the ability to miniaturize
transistors is coming to an end, there is increasing attention on new
approaches to computation, including renewed enthusiasm around the potential of
neural computation. This paper describes how recent advances in
neurotechnologies, many of which have been aided by computing's rapid
progression over recent decades, are now reigniting this opportunity to bring
neural computation insights into broader computing applications. As we
understand more about the brain, our ability to motivate new computing
paradigms with continue to progress. These new approaches to computing, which
we are already seeing in techniques such as deep learning and neuromorphic
hardware, will themselves improve our ability to learn about the brain and
accordingly can be projected to give rise to even further insights. This paper
will describe how this positive feedback has the potential to change the
complexion of how computing sciences and neurosciences interact, and suggests
that the next form of exponential scaling in computing may emerge from our
progressive understanding of the brain.
