How neurons encode different percepts is one of the most intri- guing questions in neuroscience. Two extreme hypotheses are schemes based on the explicit representations by highly selective (cardinal, gnostic or grandmother) neurons and schemes that rely on an implicit representation over a very broad and distributed popu- lation of neurons1–4,6. In the latter case, recognition would require the simultaneous activation of a large number of cells and therefore we would expect each cell to respond to many pictures with similar basic features. This is in contrast to the sparse firing we observe, because most MTL cells do not respond to the great majority of images seen by the patient. Furthermore, cells signal a particular individual or object in an explicit manner27, in the sense that the presence of the individual can, in principle, be reliably decoded from a very small number of neurons. We do not mean to imply the existence of single neurons coding uniquely for discrete percepts for several reasons: first, some of these units responded to pictures of more than one individual or object; second, given the limited duration of our recording sessions, we can only explore a tiny portion of stimulus space; and third, the fact that we can discover in this short time some images —such as photographs of Jennifer Aniston —that drive the cells suggests that each cell might represent more than one class of images. Yet, this subset of MTL cells is selectively activated by different views of individuals, landmarks, animals or objects. This is quite distinct from a completely distributed population code and suggests a sparse, explicit and invariant encoding of visual percepts in MTL. Such an abstract representation, in contrast to the metric representation in the early stages of the visual pathway, might be important in the storage of long-term memories.