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