中文摘要：

目的 基于工作记忆事件中大鼠前额叶皮层神经元电活动的非负稀疏矩阵分解（NMFs），研究如何在更高的精度上表达神经元集群。方法实验数据为工作记忆事件参考点前后5S的神经元群体电活动。时间窗口为200ms，移动步长为50ms，从初始点开始，逐个移动窗口，计算每个窗口内的每个神经元发放个数，并进行归一，即为神经元电活动矩阵；对神经元电活动矩阵进行非负稀疏矩阵分解，得到混合矩阵和具有稀疏度约束的源分量矩阵；选取特征源分量，对其进行NMFs逆变换，获取能够反映神经元集群特征的稀疏神经元电活动矩阵。结果分别对2只大鼠的10组数据进行分析，选取源分量的个数分别为10和15。由特征源分量重建的稀疏神经元电活动中，出现了明显的神经元集群；且神经元集群的时空位置与频率编码相比，更加精确。结论工作记忆事件由神经元集群进行编码；NMFs能有效、鲁棒地揭示神经元群体的稀疏发放模式；NMFs去除大量冗余，与频率编码相比，能在一个更高精度上表达神经元集群，有助于更加准确地推断皮层神经元发放模式与工作记忆事件之间的关系。

英文摘要：

Objective To analyze neural activity of in rat prefrontal cortex with the use of nonnegative matrix faetorization with sparseness constrains （NMFs） as a methodology and to study how to express neural ensemble with higher precision during working memory task. Methods Experiment data were obtained from neural population activity in the period 5 s before and after the working memory event. From the zero point, the neuronal firing times were binned in windows of 200 ms with 50 ms overlapping. The normalized neuronal bin-count matrix is decomposed by NMFs into mixing matrix and source component matrix with sparseness constraints. Meaningful components were extracted to reconstruct the input by an inverse of NMFs transform. Results By analyzing the ten groups of data from 2 rats, with the numbers of the sparse sources of 10 and 15 respectively, explicit neural ensembles with the feature components were obtained in the sparse reconstructed activity. Comparing to rate coding, the spatiotemporal location of neural ensemble was more precisely detected. Conclusion The working memory information is encoded with neural ensemble activity. NMFs could find the sparse firing pattern robustly in neuron population activity. NMFs removes much redundancy and demonstrate the possibility to express neural ensemble with higher precision compared with rate coding, which would be helpful to infer correlations between cortical firing pattern and working memory event.