Research reportFunctional connectivity among spike trains in neural assemblies during rat working memory task

•Multi-electrode neural recording in PFC of free-moving rats during a working memory task.•Functional connectivity analysis in PFC spike networks via maximum likelihood estimation.•A feature network was constructed by connections among active neurons in neural assembly.•Connection strength and global efficiency in the PFC network increased in correct trials.•Characteristics in PFC spike networks can be highlighted in the feature space.

Working memory refers to a brain system that provides temporary storage to manipulate information for complex cognitive tasks. As the brain is a more complex, dynamic and interwoven network of connections and interactions, the questions raised here: how to investigate the mechanism of working memory from the view of functional connectivity in brain network? How to present most characteristic features of functional connectivity in a low-dimensional network? To address these questions, we recorded the spike trains in prefrontal cortex with multi-electrodes when rats performed a working memory task in Y-maze. The functional connectivity matrix among spike trains was calculated via maximum likelihood estimation (MLE). The average connectivity value Cc, mean of the matrix, was calculated and used to describe connectivity strength quantitatively. The spike network was constructed by the functional connectivity matrix. The information transfer efficiency Eglob was calculated and used to present the features of the network. In order to establish a low-dimensional spike network, the active neurons with higher firing rates than average rate were selected based on sparse coding. The results show that the connectivity Cc and the network transfer efficiency Eglob vaired with time during the task. The maximum values of Cc and Eglob were prior to the working memory behavior reference point. Comparing with the results in the original network, the feature network could present more characteristic features of functional connectivity.