用于大脑图像计算和大脑网络计算的生成人工智能:一篇评论
Changwei Gong1,2, Changhong Jing1,2, Xuhang Chen1,3
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Frontiers in neuroscience
|June 29, 2023
概括
生成型人工智能 (AI) 正在推进大脑成像和网络分析. 这篇评论探讨了人工智能模型,任务和神经成像方面的挑战,以更好地提取大脑特征和连接重建.
科学领域:
- 神经成像是一种神经成像.
- 人工智能的人工智能
- 计算神经科学是一种神经科学.
背景情况:
- 非侵入性脑成像技术已经取得了显著的进步.
- 生成型人工智能 (AI) 已经大幅增长,从现有模式创建新的数据.
- 整合生成AI与神经成像为大脑研究提供了新的可能性.
研究的目的:
- 审查高级生成性AI模型,任务,挑战和大脑成像和网络计算的前景.
- 为提供当前神经成像中生成人工智能技术的全面概述.
- 专注于该领域的新方法论方法和应用.
主要方法:
- 讨论了四种经典生成模型的基本理论和算法.
- 系统地调查和分类任务,包括联合注册,超分辨率,增强,分类,细分,交叉模式,大脑网络分析和大脑解码.
- 在最新的工作中突出挑战和未来方向.
主要成果:
- 在神经成像中确定了关键的生成AI应用,例如特征提取和网络重建.
- 在脑成像中应用生成性AI的各种任务进行了分类.
- 提供了对神经科学中生成人工智能的当前状态和潜力的见解.
结论:
- 神经成像中的生成人工智能是一个有前途的领域,有很大的潜力来推进大脑映射和网络分析.
- 需要进一步的研究来应对当前的挑战,并探索未来的方向.
- 本综述为生成性AI和神经成像研究人员提供了基础的理解.
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