Encoding of rat working memory by power of multi-channel local field potentials via sparse non-negative matrix factorization
Encoding of rat working memory by power of multi-channel local field potentials via sparse non-negative matrix factorization
复制标题
通过稀疏非负矩阵分解利用多通道局部场电位对大鼠工作记忆进行编码
DOI：
10.1007/s12264-013-1333-z
复制
发表时间：
2013-06-01
影响因子：
5.6
通讯作者：
Tian, Xin
中科院分区：
文献类型：
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作者：
Liu, Xu;Liu, Tiao-Tiao;Tian, Xin
工作记忆在人类认知过程中起着重要的作用。本研究采用基于稀疏非负矩阵分解（SNMF）的多通道局部场电位（LFPs）功率对工作记忆进行编码。SNMF被用来提取特征的LFPs记录从四个Sprague-Dawley大鼠的前额叶皮层在记忆任务中的Y迷宫，每只大鼠10次试验。然后，功率增加的LFP组件被选为工作记忆相关的功能和其他组件被删除。在此基础上，利用SNMF的逆运算研究了工作记忆在时频域的编码。我们证明了theta和gamma功率在工作记忆任务中显著增加。结果表明，稀疏活动模型能较好地模拟突触后活动。theta和gamma带对编码工作记忆有意义。
Working memory plays an important role in human cognition. This study investigated how working memory was encoded by the power of multi-channel local field potentials (LFPs) based on sparse nonnegative matrix factorization (SNMF). SNMF was used to extract features from LFPs recorded from the prefrontal cortex of four Sprague-Dawley rats during a memory task in a Y maze, with 10 trials for each rat. Then the power-increased LFP components were selected as working memory-related features and the other components were removed. After that, the inverse operation of SNMF was used to study the encoding of working memory in the time-frequency domain. We demonstrated that theta and gamma power increased significantly during the working memory task. The results suggested that postsynaptic activity was simulated well by the sparse activity model. The theta and gamma bands were meaningful for encoding working memory.