{"doi":"10.1101/2023.11.12.566754","title":"Multi-timescale reinforcement learning in the brain","abstract":"Abstract To thrive in complex environments, animals and artificial agents must learn to act adaptively to maximize fitness and rewards. Such adaptive behavior can be learned through reinforcement learning 1 , a class of algorithms that has been successful at training artificial agents 2–6 and at characterizing the firing of dopamine neurons in the midbrain 7–9 . In classical reinforcement learning, agents discount future rewards exponentially according to a single time scale, controlled by the discount factor. Here, we explore the presence of multiple timescales in biological reinforcement learning. We first show that reinforcement agents learning at a multitude of timescales possess distinct computational benefits. Next, we report that dopamine neurons in mice performing two behavioral tasks encode reward prediction error with a diversity of discount time constants. Our model explains the heterogeneity of temporal discounting in both cue-evoked transient responses and slower timescale fluctuations known as dopamine ramps. Crucially, the measured discount factor of individual neurons is correlated across the two tasks suggesting that it is a cell-specific property. Together, our results provide a new paradigm to understand functional heterogeneity in dopamine neurons, a mechanistic basis for the empirical observation that humans and animals use non-exponential discounts in many situations 10–14 , and open new avenues for the design of more efficient reinforcement learning algorithms.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2023,"id":389724,"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":21,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9592,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1160307,"name":"Pablo Tano","orcid":"0000-0002-5665-8050","position":1,"is_corresponding":false},{"id":232533,"name":"HyungGoo R. Kim","orcid":"0000-0002-9106-4960","position":2,"is_corresponding":false},{"id":232534,"name":"Athar N. Malik","orcid":"0000-0002-1545-4107","position":3,"is_corresponding":false},{"id":308409,"name":"Alexandre Pouget","orcid":"0000-0003-3054-6365","position":4,"is_corresponding":false},{"id":232541,"name":"Naoshige Uchida","orcid":"0000-0002-5755-9409","position":5,"is_corresponding":false},{"id":267170,"name":"Paul Masset","orcid":"0000-0003-2001-7515","position":0,"is_corresponding":true}],"reference_count":84,"raw_metadata":null,"created_at":"2026-07-19T01:18:27.054415Z","pmid":"38014166","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":[]}