An electrophysiological basis for human memory
Memory is a fundamentally important process that guides our future behavior based on past experience. Its importance is underscored by the fact that a major feature of many neurodegenerative disorders is memory loss, which is disabling to an increasing portion of the aging population. However, the underlying electrophysiological processes underlying memory formation and retrieval in humans remains very poorly understood, and in turn, limits our abilities to provide effective therapy for patients suffering from these disorders. Here, we endeavored to investigate the underpinnings of human memory through intracranial recordings in human epilepsy patients undergoing routine monitoring for potential resective surgery. This unprecedented access to the human brain during awake behavior allowed us to make several inroads into understanding human memory. First, we investigated fast frequency oscillations in the brain, termed ripples, and their relevance during human episodic memory. We found that during a paired associates verbal memory task, ripples coupled between the medial temporal lobe (MTL) memory system and the temporal association cortex, and this coupling preceded the reinstatement of memory representations from the memory encoding period. Next, we measured single unit spiking activity from anterior temporal lobe in order to examine if temporal patterns of activity may serve as a general neural code that is replayed during memory retrieval. We found that verbal memories corresponded to item-specific sequences of cortical spiking activity, these sequences replayed during memory retrieval, and replay was preceded by ripples in the MTL. Finally, to develop a more mechanistic understanding of our findings, we used a randomly connected recurrent leaky integrate and fire neural network model to investigate the characteristics needed for significant spike sequence generation. We found that randomly connected networks can generate sequences under many parameter regimes with just white noise inputs, the specific output sequence was inherently related to the connectivity of the network, and these models could make quantitative predictions about dynamic excitatory and inhibitory balance during spiking sequences in the human data. Taken together, our results demonstrate a flexible mode of communication between the MTL and cortex in the service of episodic memory, and we provide a theoretical framework for understanding the generation of these neural patterns in the human cortex.
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