{"doi":"10.1038/s41586-024-07643-2","title":"Semantic encoding during language comprehension at single-cell resolution","abstract":"<jats:title>Abstract</jats:title><jats:p>From sequences of speech sounds<jats:sup>1,2</jats:sup> or letters<jats:sup>3</jats:sup>, humans can extract rich and nuanced meaning through language. This capacity is essential for human communication. Yet, despite a growing understanding of the brain areas that support linguistic and semantic processing<jats:sup>4–12</jats:sup>, the derivation of linguistic meaning in neural tissue at the cellular level and over the timescale of action potentials remains largely unknown. Here we recorded from single cells in the left language-dominant prefrontal cortex as participants listened to semantically diverse sentences and naturalistic stories. By tracking their activities during natural speech processing, we discover a fine-scale cortical representation of semantic information by individual neurons. These neurons responded selectively to specific word meanings and reliably distinguished words from nonwords. Moreover, rather than responding to the words as fixed memory representations, their activities were highly dynamic, reflecting the words’ meanings based on their specific sentence contexts and independent of their phonetic form. Collectively, we show how these cell ensembles accurately predicted the broad semantic categories of the words as they were heard in real time during speech and how they tracked the sentences in which they appeared. We also show how they encoded the hierarchical structure of these meaning representations and how these representations mapped onto the cell population. Together, these findings reveal a finely detailed cortical organization of semantic representations at the neuron scale in humans and begin to illuminate the cellular-level processing of meaning during language comprehension.</jats:p>","journal":"Nature","year":2024,"id":617079,"datarank":1.503897944246368,"base_score":4.204692619390966,"endowment":4.204692619390966,"self_citation_contribution":0.6307038929086449,"citation_network_contribution":0.8731940513377232,"self_endowment_contribution":0.6307038929086449,"citer_contribution":0.8731940513377232,"corpus_percentile":null,"corpus_rank":null,"citation_count":66,"citer_count":57,"citers_with_citation_signal":29,"citers_with_endowment":29,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":960238,"name":"Benjamin L. 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Here we recorded from single cells in the left language-dominant prefrontal cortex as participants listened to semantically diverse sentences and naturalistic stories. By tracking their activities during natural speech processing, we discover a fine-scale cortical representation of semantic information by individual neurons. These neurons responded selectively to specific word meanings and reliably distinguished words from nonwords. Moreover, rather than responding to the words as fixed memory representations, their activities were highly dynamic, reflecting the words’ meanings based on their specific sentence contexts and independent of their phonetic form. Collectively, we show how these cell ensembles accurately predicted the broad semantic categories of the words as they were heard in real time during speech and how they tracked the sentences in which they appeared. We also show how they encoded the hierarchical structure of these meaning representations and how these representations mapped onto the cell population. Together, these findings reveal a finely detailed cortical organization of semantic representations at the neuron scale in humans and begin to illuminate the cellular-level processing of meaning during language comprehension.</jats:p>","is_dataset_classified":null,"base_score":4.1588830833596715,"endowment":4.1588830833596715,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"38961302","pmcid":"PMC11254762","openalex_id":"https://openalex.org/W4400271414","authors":[],"funders":[{"funder_name":"NIDCD NIH HHS","grant_id":"R01 DC016950","title":null},{"funder_name":"NINDS NIH HHS","grant_id":"R25 NS065743","title":null},{"funder_name":"NIMH NIH HHS","grant_id":"R44 MH125700","title":null},{"funder_name":"NINDS NIH HHS","grant_id":"K12 NS129164","title":null},{"funder_name":"NIMH NIH HHS","grant_id":"P50 MH119467","title":null},{"funder_name":"NINDS NIH HHS","grant_id":"U01 NS121616","title":null},{"funder_name":"NINDS NIH HHS","grant_id":"UG3 NS123723","title":null},{"funder_name":"NIDCD NIH HHS","grant_id":"R01 DC019653","title":null},{"funder_name":"NINDS NIH HHS","grant_id":"U01 NS121471","title":null}],"total_grants":9,"fwci":14.8474,"citation_percentile":0.99585742,"influential_citations":0,"citation_trend":[{"year":2024,"count":7},{"year":2025,"count":31},{"year":2026,"count":25}],"oa_status":"hybrid","license":"cc-by","oa_locations":[{"url":"https://www.nature.com/articles/s41586-024-07643-2.pdf","host_type":"journal"},{"url":"https://www.nature.com/articles/s41586-024-07643-2.pdf","host_type":"publisher"},{"url":"https://www.nature.com/articles/s41586-024-07643-2","host_type":"publisher"},{"url":"https://doi.org/10.1038/s41586-024-07643-2","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/38961302","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11254762","host_type":"repository"},{"url":"https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37379384","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC11254762","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC11254762?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["Neurobiology of Language and Bilingualism","Ferroelectric and Negative Capacitance Devices","Neural dynamics and brain function"],"mesh_terms":["Adult","Aged","Female","Humans","Language","Male","Middle Aged","Neurons","Phonetics","Semantics","Speech Perception","Prefrontal Cortex","Comprehension","Narration","Young Adult","Single-Cell Analysis"],"keywords":["Encoding (memory)","Comprehension","Computer science","Resolution (logic)","Natural language processing","Artificial intelligence","Programming language"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Quality Education"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-03T00:47:38.590401Z","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":[]}