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 timefrequency 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.
Neuronal ensemble activity codes working memory.In this work,we developed a neuronal ensemble sparse coding method,which can effectively reduce the dimension of the neuronal activity and express neural coding.Multichannel spike trains were recorded in rat prefrontal cortex during a work memory task in Y-maze.As discretesignals,spikes were transferred into cont inuous signals by estinating entropy.Then the normalized continuous signals were decomposed via non-negative sparse met hod.The non-negative components were extracted to reconstruct a low-dimensional ensemble,while none of the feature components were missed.The results showed that,for well-trained rats,neuronal ensemble activities in the prefrontal cortex changed dynamically during the.working memory task.And the neuronal ensemble is more explicit via using non-negative sparse coding.Our results indicate that the neuronal ensemblesparse coding method can effectively reduce the dimnension of neuronal activity and it is a useful tool to express neural coding.
