{"title":"The visual evoked potential is a sensitive and powerful measure of experience-dependent visual cortical plasticity in mice","authors":"Jeffrey P. Gavornik , Mark F. Bear","doi":"10.1016/j.conb.2025.103019","DOIUrl":null,"url":null,"abstract":"<div><div>Despite the explosion of high-tech methods to measure activity in the mouse visual cortex, the venerable visually evoked potential (VEP) continues to prove its worth as a sensitive measure of experience-dependent cortical plasticity. The VEP recorded in layer 4 is a good estimate of the strength of feedforward synaptic excitation, and changes in amplitude correspond closely to changes in the peak firing rate of principal cells. Chronic recording of VEPs in awake mice have enabled longitudinal study of modifications induced by selective visual experience or deprivation, and these have revealed several novel forms of plasticity. The VEP provides a good estimate of spatial acuity that compares well with values obtained by behavioral approaches. Furthermore, recordings of the local field potential through the same electrodes reveal changes in oscillatory activity that reflect differential recruitment of inhibitory networks. Thus, the VEP remains a powerful tool for the study of visual cortical plasticity.</div></div>","PeriodicalId":10999,"journal":{"name":"Current Opinion in Neurobiology","volume":"93 ","pages":""},"PeriodicalIF":4.8000,"publicationDate":"2025-04-12","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/S0959438825000509","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
Despite the explosion of high-tech methods to measure activity in the mouse visual cortex, the venerable visually evoked potential (VEP) continues to prove its worth as a sensitive measure of experience-dependent cortical plasticity. The VEP recorded in layer 4 is a good estimate of the strength of feedforward synaptic excitation, and changes in amplitude correspond closely to changes in the peak firing rate of principal cells. Chronic recording of VEPs in awake mice have enabled longitudinal study of modifications induced by selective visual experience or deprivation, and these have revealed several novel forms of plasticity. The VEP provides a good estimate of spatial acuity that compares well with values obtained by behavioral approaches. Furthermore, recordings of the local field potential through the same electrodes reveal changes in oscillatory activity that reflect differential recruitment of inhibitory networks. Thus, the VEP remains a powerful tool for the study of visual cortical plasticity.
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