Long-Term Reactivation of Multiple Sub-Assemblies in the Hippocampus and Prefrontal Cortex

Abstract

In resting or sleep periods following a task, neurons in various parts of the brain become reactivated, with firing patterns often similar to that during the task. This reactivation plays an essential role in memory consolidation. However, detecting these reactivating episodes has been challenging because not all neurons recorded may be directly relevant to the memory being consolidated. Here, we propose a novel spike train clustering (STC) method for detecting groups of neurons (clusters) with partial synchronous firing. From a mechanistic standpoint, the correlated activity of an ensemble can arise from neuronal interactions within the recorded local ensemble, common external inputs, or both. To quantify these contributions, we propose to use information geometry (IG) by taking advantage of its capacity for orthogonal decomposition of neural interactions. We analyzed simultaneous single unit activity from rat medial prefrontal cortex (mPFC) and area CA1 of the hippocampus when animals explored novel objects and when they slept before and after the exploration. We demonstrate that multiple reactivations by different subsets of neurons (clusters) occurred. Those reactivations could extend over 11 hours, the entire recording duration of post-task rest after the exploration epoch. Long-lasting reactivation was not detected when all neurons were included as a single cluster in the analysis. We also showed that pairwise interactions in reactivating clusters tended to be more strongly modified than in non-reactivating ones. In addition, the pairwise interactions of the reactivating clusters in CA1 were strongly modulated by the task experience but not in the mPFC. These results indicate that hippocampal reactivation following novel experience is likely to be induced within the hippocampal circuits. In contrast, mPFC reactivation is likely to be driven by external inputs, possibly in part from the hippocampus.