  One of the most fundamental questions in Biology or Artificial Intelligence
is how the human brain performs mathematical functions. How does a neural
architecture that may organise itself mostly through statistics, know what to
do? One possibility is to extract the problem to something more abstract. This
becomes clear when thinking about how the brain handles large numbers, for
example to the power of something, when simply summing to an answer is not
feasible. In this paper, the author suggests that the maths question can be
answered more easily if the problem is changed into one of symbol manipulation
and not just number counting. If symbols can be compared and manipulated, maybe
without understanding completely what they are, then the mathematical
operations become relative and some of them might even be rote learned. The
proposed system may also be suggested as an alternative to the traditional
computer binary system. Any of the actual maths still breaks down into binary
operations, while a more symbolic level above that can manipulate the numbers
and reduce the problem size, thus making the binary operations simpler. An
interesting result of looking at this is the possibility of a new fractal
equation resulting from division, that can be used as a measure of good fit and
would help the brain decide how to solve something through self-replacement and
a comparison with this good fit.
