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 discrete signals, spikes were transferred into continuous signals by estimating entropy. Then the normalized continuous signals were decomposed via non-negative sparse method. 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 welltrained 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 ensemble sparse coding method can effectively reduce the dimension of neuronal activity and it is a useful tool to express neural coding.