Non-negative matrix factorization with sparseness constraints for neural activity in rat prefrontal cortex during working memory task
10.3760/cma.j.issn.1673-4181.2011.02.002
- VernacularTitle: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
- Author:
Yunhua XU
;
Wenwen BAI
;
Xin TIAN
- Publication Type:Journal Article
- Keywords:
Non-negative matrix factorization with sparseness constrains;
Neural firing activity;
Neural ensemble;
Working memory event
- From:
International Journal of Biomedical Engineering
2011;34(2):71-73,90,后插2
- CountryChina
- Language:Chinese
-
Abstract:
Objective To analyze neural activity of in rat prefrontal cortex with the use of nonnegative matrix factorization 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.
