MOSAIC:一种可扩展的fMRI数据集聚合和人类视觉建模框架
bioRxiv : the preprint server for biology
|January 23, 2026
概括
MOSAIC汇总了多个fMRI数据集,以创建人类视觉的大规模计算模型. 这种方法提高了解码准确性和稳定性,使得能够对不同受试者和数据集的大脑活动进行可靠的预测.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 机器学习 机器学习
背景情况:
- 大规模视觉fMRI数据集对于神经科学研究至关重要.
- 单个数据集在高级建模中面临规模上的限制.
- 现有的数据集往往是分散的,阻碍了全面的分析.
研究的目的:
- 介绍MOSAIC,这是一个聚合多种fMRI数据集的框架.
- 能够实现计算密集型建模和强大的泛化测试.
- 促进大规模,社区驱动的人类视觉模型的创建.
主要方法:
- 汇总了八个大规模视觉fMRI数据集 (93个受试者,430,007个fMRI刺激对).
- 实施了共享的预处理管道和过的测试列车分割.
- 开发了一个框架,用于后期整合额外的数据集.
主要成果:
- 感知多样化的刺激集可以提高解码精度和稳定性.
- 联合训练的脑优化编码模型预测了视觉皮层和整个大脑的fMRI活动.
- 在 silico 实验中,通过恢复对象特定的皮质区域来验证模型.
结论:
- MOSAIC提供了一个可扩展的解决方案,用于构建强大的,大规模的人类视觉模型.
- 该框架支持社区驱动的扩张和适应.
- 结果为未来的fMRI刺激集设计提供了信息,以改善模型性能.
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