Data from: Dynamic reorganization of neuronal activity patterns in parietal cortex dataset

Neuronal representations change as associations are learned between
sensory stimuli and behavioral actions. However, it is poorly understood
whether representations for learned associations stabilize in cortical
association areas or continue to change following learning. We tracked the
activity of posterior parietal cortex neurons for a month as mice stably
performed a virtual-navigation task. The relationship between cells’
activity and task features was mostly stable on single days but underwent
major reorganization over weeks. The neurons informative about task
features (trial type and maze locations) changed across days. Despite
changes in individual cells, the population activity had statistically
similar properties each day and stable information for over a week. As
mice learned additional associations, new activity patterns emerged in the
neurons used for existing representations without greatly affecting the
rate of change of these representations. We propose that dynamic neuronal
activity patterns could balance plasticity for learning and stability for
memory.