A biophysical computational model for memory trace transfer from hippocampus to neocortex

Abstract

The hippocampus plays important roles in memory formation and retrieval through sharp-wave-ripples. Recent studies have shown that certain neuron populations in the prefrontal cortex exhibit coordinated reactivations during awake ripple events. Also, the reactivation seems stronger during initial awake learning. These experimental findings suggest that the awake ripple is an important biomarker, through which the hippocampus interacts with the neocortex to assist the memory formation and retrieval. However, the computational mechanisms of this ripple based hippocampal-cortical coordination are still not clear. In this work, we build a biophysical model that includes both CA1 and layer V networks of the prefrontal cortex to investigate the possible mechanisms, by which the memory traces in the hippocampus can be transferred to prefrontal cortex. We first show that the local field potentials generated in the hippocampus and prefrontal cortex exhibit ripple range activities that are consistent with the recent experimental studies. Then, we find that the sequence information stored in the hippocampus can be successfully transferred to the prefrontal cortex recurrent networks through spike-timing dependent plasticity (STDP) and sequence replays. Further, we investigate the mechanisms of memory retrieval in the PFC network. Our findings suggest that the stored memory traces in the prefrontal cortex network can be retrieved through two different mechanisms, namely the cell-specific input and non-specific spontaneous background noise. Finally, we show that more SWRs and an optimal background noise level will both contribute to better sequence reactivations in the PFC network during memory retrieval. Our study presents a possible explanation for the memory trace transfer from the hippocampus to the neocortex through ripple coupling in awake states and reports two different mechanisms by which the stored memory traces can be successfully retrieved.\n\nAuthor SummaryThe hippocampal-cortical interactions have been found to be important for learning and memory. Recent experimental work reports interesting coordinations between the hippocampus and neocortex during ripple events in quiet awake states. Investigating this phenomena is critical for a deeper understanding of the mechanisms by which hippocampus contributes to the long-term memory formation in the neocortex. To this end, we build a biophysical computational model for both the hippocampal CA1 and prefrontal cortex networks. We demonstrate that under STDP rule and sequential inputs, the memory traces initially stored in the hippocampus can be transferred to the neocortex through the ripple events. The stored memory traces in the PFC network can be reactivated by two different mechanisms, namely the cell-specific inputs and non-specific background noises. Our study suggests that both the ripple event numbers and the level of background noises jointly determine the quality of memory retrieval during reactivation.