{"doi":"10.1101/2020.06.15.152504","title":"A learned map for places and concepts in the human MTL","abstract":"Abstract Distinct lines of research in both humans and animals point to a specific role of the hippocampus in both spatial and episodic memory function. The discovery of concept cells in the hippocampus and surrounding medial-temporal lobe (MTL) regions suggests that the MTL maps physical and semantic spaces with a similar neural architecture. Here, we studied the emergence of such maps using MTL micro-wire recordings from 20 patients navigating a virtual environment featuring salient landmarks with established semantic meaning. We present several key findings: The array of local field potentials in the MTL contains sufficient information to decode subjects’ instantaneous location in the environment. Closer examination revealed that the field potentials represent both the subjects’ locations in virtual space and in high-dimensional semantic space. We further show that both spatial and semantic representations strengthen over time. This learning effect appears as subjects increase their knowledge of the environment’s spatial and semantic layout. Similarly, we observe a learning effect on temporal sequence coding. Over time, field potentials come to represent future locations, even after controlling for spatial proximity. This predictive coding of future states, more so than the strength of spatial representations per se, explains variability in subjects’ navigation performance. Our results thus support the conceptualization of the MTL as a memory space, building semantic knowledge, spatial and non-spatial, from episodic experience to plan future actions and predict their outcomes. Significance statement Using rare micro-wire recordings, we studied the representation of spatial, semantic and temporal information in the human MTL. Our findings demonstrate that subjects acquire a cognitive map that simultaneously represents the spatial and semantic relations between landmarks. We further show that the same learned representation is used to predict future states, implicating MTL cell assemblies as the building blocks of prospective memory functions.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2020,"id":126895,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9395,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":574915,"name":"Lukas Kunz","orcid":"0000-0002-0665-7703","position":1,"is_corresponding":false},{"id":347737,"name":"Daniel R. Schonhaut","orcid":"0000-0001-8667-031X","position":2,"is_corresponding":false},{"id":348619,"name":"Armin Brandt","orcid":"0000-0001-5217-0325","position":3,"is_corresponding":false},{"id":268992,"name":"Paul A. Wanda","orcid":null,"position":4,"is_corresponding":false},{"id":240425,"name":"Ashwini Sharan","orcid":"0000-0002-4759-6097","position":5,"is_corresponding":false},{"id":227186,"name":"Michael R. Sperling","orcid":"0000-0003-0708-6006","position":6,"is_corresponding":false},{"id":255537,"name":"Andreas Schulze‐Bonhage","orcid":"0000-0003-2382-0506","position":7,"is_corresponding":false},{"id":225216,"name":"Michael J. Kahana","orcid":"0000-0001-8122-9525","position":8,"is_corresponding":false},{"id":225215,"name":"Nora A. Herweg","orcid":"0000-0002-4647-7408","position":0,"is_corresponding":true}],"reference_count":34,"raw_metadata":null,"created_at":"2026-07-18T23:15:27.226519Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}