全面回顾神经科学、神经学和精神病学中基于变压器的模型

Brain-X Pub Date : 2024-04-26 DOI:10.1002/brx2.57
Shan Cong, Hang Wang, Yang Zhou, Zheng Wang, Xiaohui Yao, Chunsheng Yang
{"title":"全面回顾神经科学、神经学和精神病学中基于变压器的模型","authors":"Shan Cong,&nbsp;Hang Wang,&nbsp;Yang Zhou,&nbsp;Zheng Wang,&nbsp;Xiaohui Yao,&nbsp;Chunsheng Yang","doi":"10.1002/brx2.57","DOIUrl":null,"url":null,"abstract":"<p>This comprehensive review aims to clarify the growing impact of Transformer-based models in the fields of neuroscience, neurology, and psychiatry. Originally developed as a solution for analyzing sequential data, the Transformer architecture has evolved to effectively capture complex spatiotemporal relationships and long-range dependencies that are common in biomedical data. Its adaptability and effectiveness in deciphering intricate patterns within medical studies have established it as a key tool in advancing our understanding of neural functions and disorders, representing a significant departure from traditional computational methods. The review begins by introducing the structure and principles of Transformer architectures. It then explores their applicability, ranging from disease diagnosis and prognosis to the evaluation of cognitive processes and neural decoding. The specific design modifications tailored for these applications and their subsequent impact on performance are also discussed. We conclude by providing a comprehensive assessment of recent advancements, prevailing challenges, and future directions, highlighting the shift in neuroscientific research and clinical practice towards an artificial intelligence-centric paradigm, particularly given the prominence of Transformer architecture in the most successful large pre-trained models. This review serves as an informative reference for researchers, clinicians, and professionals who are interested in understanding and harnessing the transformative potential of Transformer-based models in neuroscience, neurology, and psychiatry.</p>","PeriodicalId":94303,"journal":{"name":"Brain-X","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.57","citationCount":"0","resultStr":"{\"title\":\"Comprehensive review of Transformer-based models in neuroscience, neurology, and psychiatry\",\"authors\":\"Shan Cong,&nbsp;Hang Wang,&nbsp;Yang Zhou,&nbsp;Zheng Wang,&nbsp;Xiaohui Yao,&nbsp;Chunsheng Yang\",\"doi\":\"10.1002/brx2.57\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This comprehensive review aims to clarify the growing impact of Transformer-based models in the fields of neuroscience, neurology, and psychiatry. Originally developed as a solution for analyzing sequential data, the Transformer architecture has evolved to effectively capture complex spatiotemporal relationships and long-range dependencies that are common in biomedical data. Its adaptability and effectiveness in deciphering intricate patterns within medical studies have established it as a key tool in advancing our understanding of neural functions and disorders, representing a significant departure from traditional computational methods. The review begins by introducing the structure and principles of Transformer architectures. It then explores their applicability, ranging from disease diagnosis and prognosis to the evaluation of cognitive processes and neural decoding. The specific design modifications tailored for these applications and their subsequent impact on performance are also discussed. We conclude by providing a comprehensive assessment of recent advancements, prevailing challenges, and future directions, highlighting the shift in neuroscientific research and clinical practice towards an artificial intelligence-centric paradigm, particularly given the prominence of Transformer architecture in the most successful large pre-trained models. This review serves as an informative reference for researchers, clinicians, and professionals who are interested in understanding and harnessing the transformative potential of Transformer-based models in neuroscience, neurology, and psychiatry.</p>\",\"PeriodicalId\":94303,\"journal\":{\"name\":\"Brain-X\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/brx2.57\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Brain-X\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/brx2.57\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brain-X","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/brx2.57","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

这篇综合评论旨在阐明基于 Transformer 的模型在神经科学、神经学和精神病学领域日益增长的影响。Transformer 架构最初是作为分析顺序数据的解决方案而开发的,如今已发展到能有效捕捉生物医学数据中常见的复杂时空关系和长程依赖关系。它在破译医学研究中错综复杂的模式方面的适应性和有效性使其成为促进我们对神经功能和失调的理解的重要工具,与传统的计算方法大相径庭。综述首先介绍了变压器架构的结构和原理。然后探讨其适用范围,从疾病诊断和预后到认知过程评估和神经解码。此外,还讨论了为这些应用量身定制的具体设计修改及其对性能的后续影响。最后,我们对最新进展、当前挑战和未来方向进行了全面评估,强调了神经科学研究和临床实践向以人工智能为中心的范式转变,特别是考虑到 Transformer 架构在最成功的大型预训练模型中的突出地位。这篇综述为有志于了解和利用基于 Transformer 的模型在神经科学、神经学和精神病学领域的变革潜力的研究人员、临床医生和专业人士提供了翔实的参考资料。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Comprehensive review of Transformer-based models in neuroscience, neurology, and psychiatry

Comprehensive review of Transformer-based models in neuroscience, neurology, and psychiatry

This comprehensive review aims to clarify the growing impact of Transformer-based models in the fields of neuroscience, neurology, and psychiatry. Originally developed as a solution for analyzing sequential data, the Transformer architecture has evolved to effectively capture complex spatiotemporal relationships and long-range dependencies that are common in biomedical data. Its adaptability and effectiveness in deciphering intricate patterns within medical studies have established it as a key tool in advancing our understanding of neural functions and disorders, representing a significant departure from traditional computational methods. The review begins by introducing the structure and principles of Transformer architectures. It then explores their applicability, ranging from disease diagnosis and prognosis to the evaluation of cognitive processes and neural decoding. The specific design modifications tailored for these applications and their subsequent impact on performance are also discussed. We conclude by providing a comprehensive assessment of recent advancements, prevailing challenges, and future directions, highlighting the shift in neuroscientific research and clinical practice towards an artificial intelligence-centric paradigm, particularly given the prominence of Transformer architecture in the most successful large pre-trained models. This review serves as an informative reference for researchers, clinicians, and professionals who are interested in understanding and harnessing the transformative potential of Transformer-based models in neuroscience, neurology, and psychiatry.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信