{"doi":"10.1101/2025.08.13.669902","title":"Guided by Noise: Correlated Variability Channels Task-Relevant Information in Sensory Neurons","abstract":"Abstract Shared trial-to-trial variability across sensory neurons is reliably reduced when perceptual performance improves, yet this variability is low-dimensional, so it could be ignored by an optimal readout mechanism. Why then is it so consistently related to behavior? We propose that shared variability both reflects circuit structure and reveals the information communicated to downstream areas . In this framework, the same connectivity that shapes signal propagation also shapes shared variability. Using a circuit model, we show that when sensory signals align with shared variability, behaviorally relevant information is amplified without compromising coding fidelity. Analyses of neural population recordings from multiple brain areas and tasks reveal that the dominant axis of shared variability consistently aligns with task-relevant stimulus features and action plans . Finally, the behavioral impact of microstimulation can be explained by the extent to which it changes projections onto the shared variability axis. These findings suggest that shared variability may illuminate, rather than obscure, the neural dimensions that guide behavior. Significance Statement The brain’s ability to use different features of sensory information flexibly across many tasks is essential for complex decision-making. Our study reveals that the correlated variability in neural responses in mid-level visual areas indicates the visual feature that is relevant for behavior. Our biologically plausible network model predicted that it is beneficial for the representation of behaviorally relevant visual information to be aligned with the correlated variability in neurons. We tested and confirmed this in five independent neural data sets. Our results suggest that trial-by-trial variability does not affect the information encoded in sensory neurons but instead is a valuable signal for us to understand which combination of the encoded features is being used by the brain to guide choices.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2025,"id":556662,"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":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9513,"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":1260362,"name":"Yunlong Xu","orcid":"0000-0003-2589-7232","position":1,"is_corresponding":false},{"id":395659,"name":"Douglas A. Ruff","orcid":"0000-0001-7228-8822","position":2,"is_corresponding":false},{"id":821755,"name":"Amy M. Ni","orcid":"0000-0002-1746-9206","position":3,"is_corresponding":false},{"id":552347,"name":"Brent Doiron","orcid":"0000-0002-6916-5511","position":4,"is_corresponding":false},{"id":395662,"name":"Marlene R. Cohen","orcid":"0000-0001-8583-4300","position":5,"is_corresponding":false},{"id":458686,"name":"Ramanujan Srinath","orcid":"0000-0002-1832-7250","position":0,"is_corresponding":true}],"reference_count":85,"raw_metadata":null,"created_at":"2026-07-19T02:55:13.130091Z","pmid":"40832196","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":[]}