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Dynamic reorganization of neuronal activity patterns in parietal cortex

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Abstract: The neuronal representations of learned associations between sensory stimuli and behavioral actions have been studied extensively. However, the contribution of neurons to these representations has been examined almost exclusively at single snapshots in time. Therefore, it remains poorly understood if neuronal representations of learned associations are stable over timescales of days and weeks. We tracked the activity of a large population of posterior parietal cortex neurons for a month as mice stably performed a navigation-based decision task in virtual reality. Cells had reliable relationships between their activity and task features on single days; however, these relationships often changed over time. The neurons that were most informative about key task features, such as the trial type and locations in the maze, differed across days. Despite changes in individual cells, the activity in the neuronal population had statistically similar properties on each day and allowed for the stable read out of task information for over a week. During learning of new behavioral associations, novel activity patterns were incorporated into the same neurons that participated in existing representations without affecting the rate of activity changes. We propose that dynamic neuronal activity patterns provide a framework to balance plasticity required for learning and stability required for memory.

This talk is part of the Computational Neuroscience series.

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