A weightless neural state machine acting as an exploratory automaton changes its position in a simulated toy world by its own actions. A popular question is asked: how might the automaton 'become conscious of' the effect of its own actions? Here we develop previously defined iconic learning in such weightless machines so that this knowledge can be achieved. Weightlessness, iconic learning are expressed in terms of state equations. Experimental results that show the conditions under which correct predictions can be obtained on a neural simulator are presented. Issues of information integration and memory implication are briefly considered at the end of the paper.
['Igor Aleksander', 'Helen Morton']
Learning State Prediction Using a Weightless Neural Explorer