结构上受约束的编码框架使用了人类自然视觉的多声元降级潜伏模型
Amin Ranjbar1,2, Amir Abolfazl Suratgar1,2, Mohammad Bagher Menhaj1,2
1Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran.
Journal of neural engineering
|July 10, 2024
概括
我们开发了一种由大脑启发的新型模型,使用功能磁共振成像 (fMRI) 信号来预测大脑活动. 这种结构约束多输出 (SCMO) 模块通过分析视觉区域内的voxel相关性来提高预测准确性.
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.
- 生物医学工程 生物医学工程
背景情况:
- 使用卷积神经网络 (CNN) 的voxel-wise视觉编码模型从fMRI信号中预测人类大脑活动.
- 现有的CNN模型模仿视觉皮层层次结构,但缺乏用于生物医学数据预测的大脑启发的准确性.
- 需要改进的模型,以捕捉语音间关系,以便更精确地预测大脑反应.
研究的目的:
- 提出一个新的脑启发模型,结构约束多输出 (SCMO) 模块,用于增强大脑活动的预测.
- 将皮质区域内的声细胞之间的同源相关性纳入,以提高响应预测的准确性.
- 用不同的特征提取技术评估SCMO模块的预测性能.
主要方法:
- 开发了SCMO模块,以利用人口活动和集体语音行为来预测个人语音智能BOLD响应.
- 在SCMO模块中创建了一个结构矩阵,以表示视觉区域内的voxel-to-voxel交互.
- 通过使用两个特征提取方法评估了SCMO模块的预测性能:循环CNN和AlexNet模型.
主要成果:
- 该SCMO模块展示了可靠的预测能力,用于多个视觉区域的大脑反应.
- 拟议的框架在预测稳定性和特征一致性方面表现优于基准模型.
- 分析显示,该模块能够捕捉皮质区域内潜在的声细胞对声细胞相互作用.
结论:
- 该SCMO模块在从fMRI数据中预测大脑活动方面取得了重大进展.
- 这种由大脑启发的方法通过建模区域语音相关性来提高预测准确性.
- 与现有模型相比,SCMO模块为视觉编码提供了更稳定,更连贯的方法.
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