I mean an equivalent to RAM. Once a specific neural net is fully trained and is just being used, it is equivalent to a black box function. Input goes in one side and output comes out the other. There is no internal state for that specific neural net.I mean I know how a neural network works.
Information only flows left to right, from one layer to the next. If the input nodes don't change, then the state of the network is static.
Cellular automata however needs to be calculated over and over again with "information" freely flowing.
My idea is to basically merge both of them. Essentially cellular automata with unique weights per pixel. The whole grid is white, then input pixels are made black, and then output pixels are monitored.
I'm just confused as to why you couldn't constantly train the network?