{"doi":"10.1101/2025.09.11.675660","title":"An AI Agent for cell-type specific brain computer interfaces","abstract":"Abstract Decoding how specific neuronal subtypes contribute to brain function requires linking extracellular electrophysiological features to underlying molecular identities, yet reliable in vivo electrophysiological signal classification remains a major challenge for neuroscience and clinical brain-computer interfaces (BCI). Here, we show that pretrained, general-purpose vision-language models (VLMs) can be repurposed as few-shot learners to classify neuronal cell types directly from electrophysiological features, without task-specific fine-tuning. Validated against optogenetically tagged datasets, this approach enables robust and generalizable subtype inference with minimal supervision. Building on this capability, we developed the BCI AI Agent (BCI-Agent), an autonomous AI framework that integrates vision-based cell-type inference, stable neuron tracking, and automated molecular atlas validation with real-time literature synthesis. BCI-Agent addresses three critical challenges for in vivo electrophysiology: (1) accurate, training-free cell-type classification; (2) automated cross-validation of predictions using molecular atlas references and peer-reviewed literature; and (3) embedding molecular identities within stable, low-dimensional neural manifolds for dynamic decoding. In rodent motor-learning tasks, BCI-Agent revealed stable, cell-type-specific neural trajectories across time that uncover previously inaccessible dimensions of neural computation. Additionally, when applied to human Neuropixels recordings–where direct ground-truth labeling is inherently unavailable–BCI-Agent inferred neuronal subtypes and validated them through integration with human single-cell atlases and literature. By enabling scalable, cell-type-specific inference of in vivo electrophysiology, BCI-Agent provides a new approach for dissecting the contributions of distinct neuronal populations to brain function and dysfunction.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2025,"id":555753,"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":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9427,"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":640070,"name":"Zuwan Lin","orcid":"0000-0001-5786-9768","position":1,"is_corresponding":false},{"id":1453063,"name":"Jong‐Min Baek","orcid":null,"position":2,"is_corresponding":false},{"id":1454590,"name":"Almir Aljović","orcid":"0000-0001-9725-6368","position":3,"is_corresponding":false},{"id":1019696,"name":"Xinhe Zhang","orcid":"0000-0002-1223-220X","position":4,"is_corresponding":false},{"id":1452622,"name":"Ariel J. Lee","orcid":"0000-0002-8549-6310","position":5,"is_corresponding":false},{"id":1454591,"name":"Wenbo Wang","orcid":"0000-0002-4821-6789","position":6,"is_corresponding":false},{"id":1454592,"name":"Jae Yong Lee","orcid":"0000-0002-4967-911X","position":7,"is_corresponding":false},{"id":1454593,"name":"Hao Shen","orcid":"0009-0004-8864-8844","position":8,"is_corresponding":false},{"id":31812,"name":"Yichun He","orcid":"0000-0001-9573-2682","position":9,"is_corresponding":false},{"id":1454594,"name":"Na Li","orcid":"0009-0009-3824-2973","position":10,"is_corresponding":false},{"id":13744,"name":"Jia Liu","orcid":"0000-0002-2070-7754","position":11,"is_corresponding":false},{"id":1452619,"name":"Arnau Marin‐Llobet","orcid":"0009-0008-8128-4119","position":0,"is_corresponding":true}],"reference_count":49,"raw_metadata":null,"created_at":"2026-07-19T02:55:03.976486Z","pmid":"41000855","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":[]}