{"title":"贝叶斯估计的方向和方向调谐捕获参数的不确定性。","authors":"Zongting Wu, Stephen D Van Hooser","doi":"10.3389/fncir.2025.1542332","DOIUrl":null,"url":null,"abstract":"<p><p>This study explores the efficacy of Bayesian estimation in modeling the orientation and direction selectivity of neurons in the primary visual cortex (V1). Unlike traditional methods such as least squares, Bayesian estimation adeptly handles the probabilistic nature of neuronal responses, offering robust analysis even with limited data and weak selectivity. Through the analysis of both simulated and experimental data, we demonstrate that Bayesian estimation not only accurately fits the neuronal tuning curves but also effectively captures parameter certainty or uncertainty of both strongly and weakly selective neurons. Our results affirm the complex interdependencies among response parameters and highlight the variability in neuronal behavior under varied stimulus conditions. Our findings provide guidance as to how many response samples are necessary for Bayesian parameter estimation to achieve reliable fitting, making it particularly suitable for studies with constraints on data availability.</p>","PeriodicalId":12498,"journal":{"name":"Frontiers in Neural Circuits","volume":"19 ","pages":"1542332"},"PeriodicalIF":3.0000,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12319010/pdf/","citationCount":"0","resultStr":"{\"title\":\"Bayesian estimation of orientation and direction tuning captures parameter uncertainty.\",\"authors\":\"Zongting Wu, Stephen D Van Hooser\",\"doi\":\"10.3389/fncir.2025.1542332\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This study explores the efficacy of Bayesian estimation in modeling the orientation and direction selectivity of neurons in the primary visual cortex (V1). Unlike traditional methods such as least squares, Bayesian estimation adeptly handles the probabilistic nature of neuronal responses, offering robust analysis even with limited data and weak selectivity. Through the analysis of both simulated and experimental data, we demonstrate that Bayesian estimation not only accurately fits the neuronal tuning curves but also effectively captures parameter certainty or uncertainty of both strongly and weakly selective neurons. Our results affirm the complex interdependencies among response parameters and highlight the variability in neuronal behavior under varied stimulus conditions. Our findings provide guidance as to how many response samples are necessary for Bayesian parameter estimation to achieve reliable fitting, making it particularly suitable for studies with constraints on data availability.</p>\",\"PeriodicalId\":12498,\"journal\":{\"name\":\"Frontiers in Neural Circuits\",\"volume\":\"19 \",\"pages\":\"1542332\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2025-07-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12319010/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Neural Circuits\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3389/fncir.2025.1542332\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Neural Circuits","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fncir.2025.1542332","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
Bayesian estimation of orientation and direction tuning captures parameter uncertainty.
This study explores the efficacy of Bayesian estimation in modeling the orientation and direction selectivity of neurons in the primary visual cortex (V1). Unlike traditional methods such as least squares, Bayesian estimation adeptly handles the probabilistic nature of neuronal responses, offering robust analysis even with limited data and weak selectivity. Through the analysis of both simulated and experimental data, we demonstrate that Bayesian estimation not only accurately fits the neuronal tuning curves but also effectively captures parameter certainty or uncertainty of both strongly and weakly selective neurons. Our results affirm the complex interdependencies among response parameters and highlight the variability in neuronal behavior under varied stimulus conditions. Our findings provide guidance as to how many response samples are necessary for Bayesian parameter estimation to achieve reliable fitting, making it particularly suitable for studies with constraints on data availability.
期刊介绍:
Frontiers in Neural Circuits publishes rigorously peer-reviewed research on the emergent properties of neural circuits - the elementary modules of the brain. Specialty Chief Editors Takao K. Hensch and Edward Ruthazer at Harvard University and McGill University respectively, are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide.
Frontiers in Neural Circuits launched in 2011 with great success and remains a "central watering hole" for research in neural circuits, serving the community worldwide to share data, ideas and inspiration. Articles revealing the anatomy, physiology, development or function of any neural circuitry in any species (from sponges to humans) are welcome. Our common thread seeks the computational strategies used by different circuits to link their structure with function (perceptual, motor, or internal), the general rules by which they operate, and how their particular designs lead to the emergence of complex properties and behaviors. Submissions focused on synaptic, cellular and connectivity principles in neural microcircuits using multidisciplinary approaches, especially newer molecular, developmental and genetic tools, are encouraged. Studies with an evolutionary perspective to better understand how circuit design and capabilities evolved to produce progressively more complex properties and behaviors are especially welcome. The journal is further interested in research revealing how plasticity shapes the structural and functional architecture of neural circuits.