Stability and Robustness of Hippocampal Representations of Space

PROJECT SUMMARY How does the brain balance the need to preserve prior knowledge with the necessity to continuously learn new information? The tradeoff between stability and plasticity is inherent in both biological and artificial learning systems constrained by finite resources and capacity. The hippocampus is a brain region critical for

memory formation and spatial learning, which can provide a powerful experimental system for characterizing this tradeoff. The role of the hippocampus in spatial cognition is supported by the finding that pyramidal neurons in this area (place cells) fire in specific locations in an environment (place fields). The population of

place cells active in an environment is believed to form a neural representation or cognitive map of that environment. Spatial learning is critical for survival and involves two competing constraints: representations of space must be plastic to enable fast learning of new environments and changes in behavioral contingencies,

and stable over time to enable recognition of familiar environments, reliable navigation, and leveraging of previous learning. How do these competing constraints affect the stability of place fields across time? The experimental characterization of the long-term stability of spatial representations in the hippocampus has

been challenging as it requires tracking the activity of multiple place cells across extended periods of time (days to weeks). We propose to use novel approaches in large-scale electrophysiology and imaging in behaving rodents to characterize which neurons change their spatial tuning and how these changes depend

on behavior. Furthermore, we will use recordings and circuit perturbations to characterize the activity patterns that predict changes in tuning stability. Our analysis will be carried out in the context of a theoretical framework for understanding the interplay between plasticity and stability of hippocampal representations.

Characterizing the evolution of neural representations is of fundamental importance in understanding how information is maintained across brain circuits and how such maintenance is perturbed in brain disorders.