Taking abstraction as the starting point, we build a complex, self-organizing fuzzy logic system. Such a system, being built on top of abstraction as the base, turns out to be just a special outcome of the laws of abstraction. As the system is self-organizing, it runs automatically towards optimization. Using such a system in neural networks, we may come as close as possible to the workings of the human brain. The abstract fuzzy optimization is seen to follow a Gaussian distribution.

There are quite a large number of differences between the way present day computers work and the way the brain works. The very way in which basic processing is done by computers varies vastly from the way the brain processes information. Computers, working on binary logic, can take into account only high or low states. They can have a number of inputs that are processed by a single processing unit. After being processed, the input(s) can have a single output or a number of outputs. On the other hand, brain cells seem to be able to process information using abstract fuzzy logic that leads to more energy optimization. In fact, estimates suggest that present day computers use up ten million times more energy to process the same amount of information that the human brain does.