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一个动作解码框架与深度神经网络相结合,用于从唤起的大脑活动的视频中预测人类行为的语义

Yuanyuan Zhang¹, Manli Tian¹, Baolin Liu¹

¹School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.

Frontiers in neuroinformatics

|March 6, 2025

概括

研究人员开发了一个新的框架,使用深度学习模型从大脑活动中解码动作语义. 这种方法将功能磁共振成像 (fMRI) 数据与视觉感知联系起来,表明动作语义在视觉皮层中普遍存在.

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
认知科学认知科学

背景情况:

功能磁共振成像 (fMRI) 研究越来越多地关注视觉知觉的语义解码.
在理解大脑活动与视频中人类行为的语义特征之间的关系方面存在差距.

研究的目的:

构建一个新的框架来从大脑活动中解码动作语义.
在视频刺激中建立大脑活动模式和人类行为语义特征之间的关系.

主要方法:

使用预训练的扩展三维 (X3D) 深度神经网络 (DNN) 进行图像动作识别.
训练有素的回归模型将fMRI脑活动与深层图像特征联系起来.
整合了非局部注意力机制和多层感知器 (MLP) 具有多任务损失,以提高解码精度.
应用数据增强技术,如线性插值,以减轻小样本大小的限制.

主要成果:

通过X3D-DNN模型提取的特征被发现是生物相关的,对感知有用.
开发的框架成功丰富了用于动作识别的语义解码模型.
在不同视觉皮层区域进行的实验表明,人类行为的语义信息分布广泛.

结论:

拟议的框架有效地从大脑活动中解码了行动语义.
这项研究证明了深度学习特征对视觉感知的生物学相关性.
行动语义信息在视觉皮层中得到广泛的代表,而不是局部化到特定区域.

关键词:

行动是语义上的行动.解码的解码方法是功能性磁共振成像技术功能性磁共振成像技术多个主体模型模型三维卷积神经网络是一个三维卷积神经网络.

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