通过让电脑学习人所看到的图像和大脑活动的关系，科学家成功将人脑活动的信息再现为图像。不久之后，利用此技术复原出梦中景象也不是不可能。
日本国际电气通信基础技术研究院脑情报研究所室长神谷之康在12月11日出版的科学杂志《神经元》上发表了这项再现大脑活动的最新研究成果。
据神谷介绍，梦和想象等人脑中出现的实际并不存在的图像，其实和看东西时一样有“视觉皮质”在发挥作用，“读出梦和想象中的图像已经不再遥远”。
神谷等人准备了400张图片，每张由纵横各10小格共100格组成，格子内用黑白两色描绘出字母、方块、十字等记号。实验中，参加者每12秒看一幅图片，随后通过功能性核磁共振仪(fMRI)测定视觉皮质脑血流变化，再利用联系图像和脑部活动的软件让电脑学习这一规律。
之后，参加者被要求观看新的图像，随后将fMRI的测定结果输入到电脑后，电脑就能结合之前学习的规律将图像基本重现。（生物谷Bioon.com）
生物谷推荐原始出处：
Neuron，Volume 60, Issue 5, 915-929，Yoichi Miyawaki，Yukiyasu Kamitani
Visual Image Reconstruction from Human Brain Activity using a Combination of Multiscale Local Image Decoders
Yoichi Miyawaki1,2,6,Hajime Uchida2,3,6,Okito Yamashita2,Masa-aki Sato2,Yusuke Morito4,5,Hiroki C. Tanabe4,5,Norihiro Sadato4,5andYukiyasu Kamitani2,3,,
1 National Institute of Information and Communications Technology, Kyoto, Japan
2 ATR Computational Neuroscience Laboratories, Kyoto, Japan
3 Nara Institute of Science and Technology, Nara, Japan
4 The Graduate University for Advanced Studies, Kanagawa, Japan
5 National Institute for Physiological Sciences, Aichi, Japan
6 These authors contributed equally to this work
Perceptual experience consists of an enormous number of possible states. Previous fMRI studies have predicted a perceptual state by classifying brain activity into prespecified categories. Constraint-free visual image reconstruction is more challenging, as it is impractical to specify brain activity for all possible images. In this study, we reconstructed visual images by combining local image bases of multiple scales, whose contrasts were independently decoded from fMRI activity by automatically selecting relevant voxels and exploiting their correlated patterns. Binary-contrast, 10 × 10-patch images (2100 possible states) were accurately reconstructed without any image prior on a single trial or volume basis by measuring brain activity only for several hundred random images. Reconstruction was also used to identify the presented image among millions of candidates. The results suggest that our approach provides an effective means to read out complex perceptual states from brain activity while discovering information representation in multivoxel patterns.
