Machine learning is the field that is dedicated to the design and development of algorithms and techniques that allow computers to “learn”. Two common types of learning that are often mentioned are supervised learning and unsupervised learning. One often understands that in supervised learning, the system is given the desired output, and it is required to produce the correct output for the given input, while in unsupervised learning the system is given only the input and the objective is to find the natural structure inherent in the input data. We, however, suggest that even with unsupervised learning, the information inside the input, the structure of the input, and the sequence that the input is given to the system actually make the learning “supervised” in some way. Therefore, we recommend that in order to make the machine learn, even in a “supervised” manner, we should use an “unsupervised learning” model together with an appropriate way of presenting the input. We propose in this paper a simple plasticity neural network model that has the ability of storing information as well as storing the association between a pair of inputs. We then introduce two simple unsupervised learning rules and a framework to supervise our neural network.