Associativity, auto-reversibility and question-answering are the three intrinsic functions to be investigated for the proposed Q'tron Neural Network (NN) model. A Q'tron NN possesses these functions due to its property of local-minima free if it is built as a known-energy system which is equipped with the proposed persistent noise-injection mechanism. The so-built Q'tron NN, as a result, will settle down if and only if it ‘feels' feasible, i.e., the energy of its state has been low enough truly. With such a nature, the NN is able to accommodate itself ‘everywhere' to reach a feasible state autonomously. Three examples, i.e., an associative adder, an N-queen solver, and a pattern recognizer are demonstrated in this paper to highlight the concept.