通过人工智能对大脑和行为动态的联合建模.
Mackenzie Weygandt Mathis1, Alexander Mathis2
1Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. mackenzie.mathis@epfl.ch.
Nature reviews. Neuroscience
|December 3, 2025
概括
这篇评论综合了联合大脑行为建模方面的进展,强调了人工智能如何揭示共享的神经和行为结构. 未来的工作应该专注于可靠性和可解释性,以实现强大的科学和工程应用.
科学领域:
- 神经科学和人工智能 人工智能
- 计算神经科学是一种神经科学.
- 机器学习用于大脑数据分析
背景情况:
- 人工智能 (AI) 推动了科学和工程领域的重大进展.
- 联合大脑行为建模整合了神经和行为数据,以了解大脑功能.
- 最近的方法创新对于推进这个跨学科领域至关重要.
研究的目的:
- 综合最近在联合大脑行为建模方面的进展.
- 专注于方法创新,科学动机和未来的研究方向.
- 探索这些模型如何揭示科学和工程的共同大脑行为结构.
主要方法:
- 关于联合大脑行为建模的最新文献的综述.
- 将建模方法分为歧视性,生成性和对比类的分类.
- 讨论行为分析方面的进展,包括姿势估计和多式联络语言模型.
主要成果:
- 联合建模方法是由歧视性,生成性和对比性框架塑造的.
- 先进的行为分析技术,如姿势估计和多模式语言模型提供了新的可能性.
- 神经和行为数据的整合为共享的基础结构提供了洞察力.
结论:
- 联合大脑行为建模为科学发现和工程应用提供了强大的工具.
- 未来的创新应该考虑模型的可靠性和可解释性以及性能.
- 人工智能和行为分析的持续发展将增强下一代联合模型.
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