matlab做信效度分析代码-Predicting-IT-cortex-output-using-DNN-and-analysis-MIT-:

上传者: 38590567 | 上传时间: 2023-06-30 01:13:44 | 文件大小: 2.45MB | 文件类型: ZIP
matlab做信效度分析代码使用深度神经网络及其分析预测下颞(IT)多单元输出。 深度神经网络由多层组成,以处理输入图像。 以类似的方式,灵长类动物大脑的视觉皮层具有多个层,这些层处理从视神经传入的视觉刺激。 它们按以下顺序排列:V1,V2,V3,V4,IT(下颞)。 IT层类似于经过训练的DNN的最后一层,确定图像中的对象。 在该项目中,比较了灵长类动物大脑的视觉皮层(V4和IT)的5个区域中的2个区域与流行的DNN模型之间的比较。 用于比较的一些DNN模型是: HMO HMAX 像V1 像V2 克里热夫斯基等。 2012年 Zeiler&Fergus 2013 1.1)数据获取和使用 在显示测试对象(灵长类动物)测试图像的同时,从其V4和IT区域记录神经输出。 V4区域具有128个通道,通过该通道收集神经输出,而IT区域具有168个通道。 因此,灵长类动物大脑中一幅图像的IT表示是一个168维向量。 总共向灵长类动物显示了1960张图像,因此V4数据矩阵为1960x128,而IT数据矩阵为1960x168。 这是数据的链接: 这里仅使用多单位数据。 为了从DNN模型的最后一个完全连

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