{"title":"Eligibility traces as a synaptic substrate for learning","authors":"Harel Z. Shouval , Alfredo Kirkwood","doi":"10.1016/j.conb.2025.102978","DOIUrl":null,"url":null,"abstract":"<div><div>Animals can learn to associate a behavior or a stimulus with a delayed reward, this is essential for survival. A mechanism proposed for bridging this gap are synaptic eligibility traces, which are slowly decaying tags, which can lead to synaptic plasticity if followed by rewards. Recently, experiments have demonstrated the existence of synaptic eligibility traces in diverse neural systems, depending on either neuromodulators or plateau potentials. Evidence for both eligibility trace-dependent potentiation and depression of synaptic efficacies has emerged. We discuss the commonalities and differences of these different results. We show why the existence of both potentiation and depression is important because these opposing forces can lead to a synaptic stopping rule. Without a stopping rule, synapses would saturate at their upper bound thus leading to a loss of selectivity and representational power. We discuss the possible underlying mechanisms of the eligibility traces as well as their functional and theoretical significance.</div></div>","PeriodicalId":10999,"journal":{"name":"Current Opinion in Neurobiology","volume":"91 ","pages":"Article 102978"},"PeriodicalIF":4.8000,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Opinion in Neurobiology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0959438825000091","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Animals can learn to associate a behavior or a stimulus with a delayed reward, this is essential for survival. A mechanism proposed for bridging this gap are synaptic eligibility traces, which are slowly decaying tags, which can lead to synaptic plasticity if followed by rewards. Recently, experiments have demonstrated the existence of synaptic eligibility traces in diverse neural systems, depending on either neuromodulators or plateau potentials. Evidence for both eligibility trace-dependent potentiation and depression of synaptic efficacies has emerged. We discuss the commonalities and differences of these different results. We show why the existence of both potentiation and depression is important because these opposing forces can lead to a synaptic stopping rule. Without a stopping rule, synapses would saturate at their upper bound thus leading to a loss of selectivity and representational power. We discuss the possible underlying mechanisms of the eligibility traces as well as their functional and theoretical significance.
期刊介绍:
Current Opinion in Neurobiology publishes short annotated reviews by leading experts on recent developments in the field of neurobiology. These experts write short reviews describing recent discoveries in this field (in the past 2-5 years), as well as highlighting select individual papers of particular significance.
The journal is thus an important resource allowing researchers and educators to quickly gain an overview and rich understanding of complex and current issues in the field of Neurobiology. The journal takes a unique and valuable approach in focusing each special issue around a topic of scientific and/or societal interest, and then bringing together leading international experts studying that topic, embracing diverse methodologies and perspectives.
Journal Content: The journal consists of 6 issues per year, covering 8 recurring topics every other year in the following categories:
-Neurobiology of Disease-
Neurobiology of Behavior-
Cellular Neuroscience-
Systems Neuroscience-
Developmental Neuroscience-
Neurobiology of Learning and Plasticity-
Molecular Neuroscience-
Computational Neuroscience