神经科学中的机器学习和人工智能:研究人员的入门教程
Fakhirah Badrulhisham1, Esther Pogatzki-Zahn2, Daniel Segelcke2
1Royal Devon and Exeter Hospital NHS Trust, Exeter, United Kingdom.
Brain, behavior, and immunity
|November 16, 2023
概括
人工智能 (AI) 和机器学习 (ML) 为分析复杂科学数据提供了强大的工具. 然而,验证AI/ML发现并了解它们的决策过程对于可靠的应用至关重要.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 越来越多地用于科学研究.
- 机器学习方法可以分析复杂的多维数据,这些数据来自诸如脑部成像和omics之类的来源.
- 传统的统计方法可能无法识别ML可以发现的模式.
研究的目的:
- 引入机器学习 (ML) 和人工智能 (AI) 的原则和常用方法.
- 讨论最新的进展,风险和ML/AI科学应用的未来方向.
- 用例子说明神经科学中的ML的实际用途和局限性.
主要方法:
- 解释了基本的ML/AI原则.
- 通常使用的ML方法的概述.
- 讨论该领域最近的方法进步.
主要成果:
- 机器学习可以识别科学数据中的复杂模式,超越传统方法.
- 存在重大局限性,包括需要外部验证和算法的"黑子"性质.
- 由于缺乏可解释性,医疗决策中可能存在道德问题.
结论:
- 机器学习和人工智能对科学发现有很大的前景,特别是在神经科学等数据丰富的领域.
- 外部验证和可解释性是负责任的ML/AI实施的关键考虑因素.
- 未来的方向包括解决局限性和扩大应用,同时考虑道德影响.
关键词:
* 漫画 漫画 漫画人工智能的人工智能是人工智能.行为研究是行为研究.机器学习是机器学习.神经科学是一个神经科学.疼痛 疼痛 疼痛 疼痛预测建模的预测建模.功能磁力共振成像 (fMRI) 是一种更多相关视频
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