Evidence map›Paper›PMID 38979377›Full record

ArticlebioRxiv : the preprint server for biology2025

Distinct dopaminergic spike-timing-dependent plasticity rules are suited to different functional roles.

Baram Sosis, Jonathan E Rubin

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Baram SosisDepartment of Mathematics, University of Pittsburgh, 301 Thackeray Hall, Pittsburgh, 15260, PA, USA.ORCID 0009-0008-0033-5691
Jonathan E RubinDepartment of Mathematics, University of Pittsburgh, 301 Thackeray Hall, Pittsburgh, 15260, PA, USA.ORCID 0000-0002-1513-1551

Funding

CRCNS: Decision dynamics in cortico-basal ganglia-thalamic networksR01DA059993 · NIDA · CARNEGIE-MELLON UNIVERSITY · PI Jonathan E. Rubin, TIMOTHY D VERSTYNEN · 2023 to 2026
$1.3M
CRCNS Circuit-Level Mechanisms of Adaptive decision-makingR01DA053014 · NIDA · CARNEGIE-MELLON UNIVERSITY · PI VERSTYNEN, TIMOTHY D · 2020 to 2022
$1.0M
Behavioral quantification through active learning and multidimensional physiological monitoringR34DA059509 · NIDA · CARNEGIE-MELLON UNIVERSITY · PI GROVER, PULKIT, KUANG, ZHENG · 2024 to 2025
$663k
NIDA NIH HHS R01 DA053014NIDA NIH HHS R01 DA059993NIDA NIH HHS R34 DA059509
6 · The paper itself

Abstract

Various mathematical models have been formulated to describe the changes in synaptic strengths resulting from spike-timing-dependent plasticity (STDP). A subset of these models include a third factor, dopamine, which interacts with spike timing to contribute to plasticity at specific synapses, notably those from cortex to striatum at the input layer of the basal ganglia. Theoretical work to analyze these plasticity models has largely focused on abstract issues, such as the conditions under which they may promote synchronization and the weight distributions induced by inputs with simple correlation structures, rather than on scenarios associated with specific tasks, and has generally not considered dopamine-dependent forms of STDP. In this paper we introduce forms of dopamine-modulated STDP adapted from previously proposed plasticity rules. We then analyze, mathematically and with simulations, their performance in two biologically relevant scenarios. We test the ability of each of the three models to complete simple value estimation and action selection tasks, studying the learned weight distributions and corresponding task performance in each setting. Interestingly, we find that each plasticity rule is well suited to a subset of the scenarios studied but falls short in others. Different tasks may therefore require different forms of synaptic plasticity, yielding the prediction that the precise form of the STDP mechanism present may vary across regions of the striatum, and other brain areas impacted by dopamine, that are involved in distinct computational functions.

Indexed as

Action selectionBasal gangliaDopamineReward predictionSTDPSynaptic plasticityValue estimation

Identifiers

PMID38979377
PMCPMC11230239

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.