Role of neural variation in hippocampal coding and plasticity

University Of California, San Francisco, San Francisco CA

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Abstract

Numerous researchers have shown that, in humans, the hippocampal formation is necessary for the[unreadable] formation of new memories of facts or events. In rodents these same structures play an essential role in an[unreadable] animal's ability to learn about and remember complex associations, including tasks where the animal must[unreadable] learn and remember information about a set of spatial cues in order to navigate through an environment.[unreadable] Both event / fact memory in humans and spatial memory in rodents requires learning complex relationships,[unreadable] and that parallel strongly suggests that qualitatively similar processing occurs in the human and the rat[unreadable] hippocampus, making the rat hippocampus an attractive model system for investigating memory processing.[unreadable] Memory storage and recall require a balance between seemingly contradictory requirements: the system[unreadable] must be sufficiently plastic to allow new patterns of activity to be rapidly stored, yet sufficiently stable to allow[unreadable] previously stored memories to be retrieved without great distortion. The aim of this proposal is to compare[unreadable] neural variability among the inputs to the hippocampus, the hippocampus itself and the outputs of the[unreadable] hippocampus and to determine whether variability is related to behavior and dynamically regulated during[unreadable] learning. We will test the following hypotheses: 1) the hippocampal circuit creates a low variability[unreadable] representation of space from highly variable inputs, 2) variability in the hippocampal circuit is decreased in[unreadable] the context of memory tasks, and 3) learning causes rapid changes in variability in the hippocampus but[unreadable] slower changes in the downstream deep layers of the entorhinal cortex (EC), reflecting the specialization of[unreadable] the hippocampus for rapid learning and of the deep EC for slower learning of the relationships between[unreadable] locations and behaviors. Integrating our findings with those of the other projects will help us better[unreadable] understand the role of variation in the neural code across brain systems.