具有大型卷积内核的大型参数框架,用于编码视觉fMRI活动信息
Shuxiao Ma1, Linyuan Wang1, Senbao Hou1
1Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou, 450000, China.
Cerebral cortex (New York, N.Y. : 1991)
|July 12, 2024
概括
视觉编码模型中的大型卷积内核增强了大脑活动预测. 使用这些内核扩展模型参数可以提高视觉功能磁共振成像数据的性能.
科学领域:
- 神经科学是一个神经科学.
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 深度神经网络用于视觉编码模型,以解释大脑对刺激的反应.
- 用大型卷积内核创建的大型受体场已被证明可以提高卷积编码模型的性能.
研究的目的:
- 在更大的参数尺度上研究大型卷积内核编码模型的性能.
- 提出一个大规模的参数框架,利用相当大的卷积内核来编码视觉功能磁共振成像 (fMRI) 活动.
主要方法:
- 采用大型内核卷积网络进行刺激图像特征提取,并增加了通道数以扩大参数大小.
- 使用多主题融合模块在训练期间扩大输入数据,以适应增加的参数.
- 开发了一个voxel映射模块,将刺激图像特征转化为fMRI信号.
主要成果:
- 拟议的框架显示,与基础规模模型相比,自然景观数据集的性能大约有7%的改善.
- 分析显示,在该框架内,编码性能和可训练性之间存在权衡.
- 在视觉编码中扩展参数被证实会产生性能增强.
结论:
- 该研究验证了视觉编码模型中增加参数,特别是大型卷积内核,可以提高性能.
- 拟议的大规模框架为编码视觉fMRI数据提供了一个有希望的方法.
- 进一步的研究可以探索在这些模型中优化性能和可训练性之间的平衡.
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