Artificial neural networks are a major component of current AI systems. While doing my own reading about them I came up with an idea of cyclic neural networks, which in first approach seemed just plain stupid. After some thought, I decided to try them out and only then draw any conclusion. So this project aims to do exactly that: prove (or negate) that Cyclic Artificial Neural Networks are useless.
Firstly, our brain (any brain, as a mater of fact) contains looped neurons. Those loops may be the perfect place for short term memory (not the one that keeps track of events, but the one used when thinking, like cache of sorts). So if such loops could be reproduced, AI would have instant memory of it's actions.
Secondly, such system can be saturated. By saturation I mean it can be filled with junk signals, propagating over and over again, until all neurons in system are active end external input no longer influences the outcome of the network.
Finally, balance condition exists. It is unique for every system and I am not sure, if it can be expressed in other ways than numericaly. If system is put into stable balance, external input may be 'considered' using available memory and (perhaps) inteligent output can be produced.