Open AccessLOW-DIMENSIONAL STRUCTURES: SPARSE CODING FOR NEURONAL ACTIVITY
Author(s) -
Yunhua Xu,
Wei Bai,
Xin Tian
Publication year - 2013
Publication title -
journal of innovative optical health sciences/journal of innovation in optical health science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.421
H-Index - 24
eISSN - 1793-5458
pISSN - 1793-7205
DOI - 10.1142/s1793545813500028
Subject(s) - neural coding , premovement neuronal activity , prefrontal cortex , working memory , computer science , neural ensemble , coding (social sciences) , pattern recognition (psychology) , neural activity , artificial intelligence , brain activity and meditation , neuroscience , electroencephalography , cognition , mathematics , psychology , statistics
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 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 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 ensemble sparse coding method can effectively reduce the dimension of neuronal activity and it is a useful tool to express neural coding