In the study of the brain, there is a hypothesis that sparse coding is realized in information representation of external stimuli, which is experimentally confirmed for visual stimulus recently. However, unlike the specific functional region in the brain, sparse coding in information processing in the whole brain has not been clarified sufficiently. In this study, we investigate the validity of sparse coding in the whole human brain by applying various matrix factorization methods to functional magnetic resonance imaging data of neural activities in the whole human brain. The result suggests sparse coding hypothesis in information representation in the whole human brain, because extracted features from sparse MF method, SparsePCA or MOD under high sparsity setting, or approximate sparse MF method, FastICA, can classify external visual stimuli more accurately than non-sparse MF method or sparse MF method under low sparsity setting.

翻译：在大脑研究中存在一个假说，即外部刺激的信息表征实现了稀疏编码，这一假说近期已在视觉刺激实验中得到证实。然而，与大脑特定功能区域不同，全脑信息处理中的稀疏编码机制尚未充分阐明。本研究通过将多种矩阵分解方法应用于全脑神经活动的功能磁共振成像数据，探讨了全脑稀疏编码的有效性。结果表明，在信息表征中稀疏编码假说在全脑层面成立——因为采用高稀疏度设置的稀疏矩阵分解方法（SparsePCA或MOD）或近似稀疏矩阵分解方法（FastICA）提取的特征，相比非稀疏矩阵分解方法或低稀疏度设置的稀疏矩阵分解方法，能更准确地对外部视觉刺激进行分类。