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. 2013 Apr 18;29(3):279–286. doi: 10.1007/s12264-013-1333-z

Encoding of rat working memory by power of multi-channel local field potentials via sparse non-negative matrix factorization

Xu Liu 11333, Tiao-Tiao Liu 21333, Wen-Wen Bai 21333, Hu Yi 21333, Shuang-Yan Li 11333, Xin Tian 11333,21333,✉
PMCID: PMC5561844  PMID: 23606209

Abstract

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.

Keywords: sparse non-negative matrix factorization, multi-channel local field potentials, working memory, prefrontal cortex

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