Representation of abstract semantic knowledge in populations of human single neurons in the medial temporal lobe

Sensory experience elicits complex activity patterns throughout the neocortex. Projections from the neocortex converge onto the medial temporal lobe (MTL), in which distributed neocortical firing patterns are distilled into sparse representations. The precise nature of these neuronal representations is still unknown. Here, we show that population activity patterns in the MTL are governed by high levels of semantic abstraction. We recorded human single-unit activity in the MTL (4,917 units, 25 patients) while subjects viewed 100 images grouped into 10 semantic categories of 10 exemplars each. High levels of semantic abstraction were indicated by representational similarity analyses (RSAs) of patterns elicited by individual stimuli. Moreover, pattern classifiers trained to decode semantic categories generalised successfully to unseen exemplars, and classifiers trained to decode exemplar identity more often confused exemplars of the same versus different categories. Semantic abstraction and generalisation may thus be key to efficiently distill the essence of an experience into sparse representations in the human MTL. Although semantic abstraction is efficient and may facilitate generalisation of knowledge to novel situations, it comes at the cost of a loss of detail and may be central to the generation of false memories. Author summary What is the neuronal code for sensory experience in the human medial temporal lobe (MTL)? Single-cell electrophysiology in the awake human brain during chronic, invasive epilepsy monitoring has previously revealed the existence of so-called concept cells. These cells have been found to increase their firing rate in response to, for example, the famous tennis player Roger Federer', whether his name is spoken by a computer voice or a picture of him is presented on a computer screen. These neurons thus seem to encode the semantic content of a stimulus, regardless of the sensory modality through which it is delivered. Previous work has predominantly focused on individual neurons that were selected based on their strong response to a particular stimulus using rather conservative statistical criteria. Those studies stressed that concept cells encode a single, concrete concept in an all-or-nothing fashion. Here, we analysed the neuronal code on the level of the entire population of neurons without any preselection. We conducted representational similarity analyses (RSAs) and pattern classification analyses of firing patterns evoked by visual stimuli (for example, a picture of an apple) that could be grouped into semantic categories on multiple levels of abstraction (fruit', food', natural things'). We found that neuronal activation patterns contain information on higher levels of categorical abstraction rather than just the level of individual exemplars. On the one hand, the neuronal code in the human MTL thus seems well suited to generalise semantic knowledge to new situations; on the other hand, it could also be responsible for the generation of false memories.