{"doi":"10.1103/physrevresearch.7.l022039","title":"Exactly solvable statistical physics models for large neuronal populations","abstract":"Maximum-entropy methods provide a principled path connecting measurements of neural activity directly to statistical physics models, and this approach has been successful for populations of <a:math xmlns:a=\"http://www.w3.org/1998/Math/MathML\"> <a:mrow> <a:mi>N</a:mi> <a:mo>∼</a:mo> <a:mn>100</a:mn> </a:mrow> </a:math> neurons. As <b:math xmlns:b=\"http://www.w3.org/1998/Math/MathML\"> <b:mi>N</b:mi> </b:math> increases in new experiments, we enter an undersampled regime where we have to choose which observables should be constrained in the maximum-entropy construction. The best choice is the one that provides the greatest reduction in entropy, defining a “minimax entropy” principle. This principle becomes tractable if we restrict attention to correlations among pairs of neurons that link together into a tree; we can find the best tree efficiently, and the underlying statistical physics models are exactly solved. We use this approach to analyze experiments on <c:math xmlns:c=\"http://www.w3.org/1998/Math/MathML\"> <c:mrow> <c:mi>N</c:mi> <c:mo>∼</c:mo> <c:mn>1500</c:mn> </c:mrow> </c:math> neurons in the mouse hippocampus, and we find that the resulting model captures key features of collective activity in the network.","journal":"Physical Review Research","year":2025,"id":529813,"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":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9476,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":692173,"name":"Qiwei Yu","orcid":"0000-0003-0610-3484","position":1,"is_corresponding":false},{"id":639158,"name":"Rich Pang","orcid":"0000-0002-2644-6110","position":2,"is_corresponding":false},{"id":859534,"name":"William Bialek","orcid":"0000-0002-7823-3862","position":3,"is_corresponding":false},{"id":290926,"name":"Stephanie E. Palmer","orcid":"0000-0001-6211-6293","position":4,"is_corresponding":false},{"id":319787,"name":"Christopher W. Lynn","orcid":"0000-0002-6487-2671","position":0,"is_corresponding":true}],"reference_count":41,"raw_metadata":null,"created_at":"2026-07-19T02:51:01.235017Z","pmid":"37904743","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":[]}