A robin is closer to the bird prototype than an ostrich is, but they are both closer to it than they are to the protype of a fish, so we call them both birds, only it takes longer to say an ostrich is a bird than it take to say a robin is a bird, because the ostrich is further from the prototype. Inputs would be sorted by this feature detector on the basis of whether or not they had the right features. The prototype theory would require that your brain build up an image or template of an idealised or prototypical member of the category. Inputs would be sorted on the basis of whether they matched more closely to the prototype of this category or to that of another category. Chairs must simply share certain features.
Neural nets are good at categorisation.
Unsupervised nets with competitive learning work more like prototype models, whereas supervised nets like backpropagation can both memorise special cases and learn features, rules and regularities, so are feature processing models. Let me give some abstract examples first from symbolic logic. In this case, the truth of A is necessary but not sufficient, the truth of B is necessary but not sufficient, and the truth of both together is both sufficient and necessary for the truth of the proposition as a whole. In this case the truth of A alone is sufficient but not necessary, and the truth of B alone is sufficient but not necessary for the truth of the proposition as a whole. The truth of both together is also sufficient, but not necessary.