  A new class of energy-efficient digital microprocessor is being developed
which is susceptible to thermal noise and consequently operates in
probabilistic rather than conventional deterministic mode. Hybrid computing
systems which combine probabilistic and deterministic processors can provide
robust and efficient tools for computational problems that hitherto would be
intractable by conventional deterministic algorithm. These developments suggest
a revised perspective on the consequences of ion-channel noise in slender
axons, often regarded as a hindrance to neuronal computations. It is proposed
that the human brain is such an energy-efficient hybrid computational system
whose remarkable characteristics emerge from constructive synergies between
probabilistic and deterministic modes of operation. In particular, the capacity
for intuition and creative problem solving appears to arise naturally from such
a hybrid system. Bearing in mind that physical thermal noise is both pure and
available at no cost, our proposal has implications for attempts to emulate the
energy-efficient human brain on conventional energy-intensive deterministic
supercomputers.
