The growth of networks that enable the storage of millions of situations in the mammalian brain and the evaluation of the relationships among them may also form the basis for representing and categorizing explicit knowledge. The same mechanisms that define unique positions and their relationships in a map can be used to define or symbolize events, objects and living things10,19. Many experiments demonstrate that recognition and recall of objects or events are associated with unique constellations of firing patterns in the entorhinal cortex–hippocampal system in a variety of species21,41–46. Examples of semantic ‘encoding’ are also available from human epilepsy patients, in which selective firing of hippocampal and entorhinal cortex neurons has been evoked by specific words, objects or individuals, largely independently of their physical characteristics47,48. The classification of items, events and situations on the basis of semantic proximity shares many features with the distance relationship among landmarks49. Similarly to the embodiment of the spatial relations among objects in the cognitive map1, models of semantic relatedness use a metric based on topological similarity and a neuronal network equivalent of vector distance
