Lu, Yi; (2020) Hippocampal Spatial Representation: Integrating Environmental and Self-motion Signals. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Electrophysiological recording in freely-moving rodents has established that place cells fire when the animal occupies a specific location and grid cells fire when at several locations, arranged on a regular triangular grid. Experiments and theories suggest that place cells and grid cells 1) receive inputs reflecting both environmental and self-motion information, and 2) are functionally connected to each other. Yet it remains elusive how the environmental and self-motion inputs dictate either place cell or grid cell firing. In a series of experiments, I address this question by manipulating the inputs independently while simultaneously recording place and grid cells activity. Firstly, I introduce our visual 2-d virtual reality system, in which mice run on an air-supported Styrofoam ball with their head held but allowed to rotate in the horizontal plane. The virtual arena is projected on surrounding screens and on the floor at a viewpoint that shifts with the rotation of the ball. With sufficient training, mice can navigate freely in the virtual environment and successfully retrieve rewards from an unmarked location. Electrophysiological data confirms that place, grid, and head-direction cells show characteristic spatial tuning in VR. In a second experiment, the gain factor that maps mice’s running speed to the visual translation of the virtual environment is manipulated. Results show that place cell firings are more driven by vision while grid cells incorporate self-motion inputs better. The last experiment had mice navigate in darkness. Without visual input co-recorded place cells and grid cells both suffer disruption in spatial tuning, albeit tuning is better preserved near to environmental boundaries. These results demonstrated that environmental and self-motion signals contribute to place and grid cells’ spatial representation of different significance, and constrain models with presumptions about how the place cells and grid cells integrate inputs and interact with each other.

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