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Title: Neural coding of representations of self-location
Author: Towse, Benjamin William
ISNI:       0000 0004 7429 2587
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2018
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Grid cells in the hippocampal formation fire when the animal visits nodes of a triangular grid covering its environment. Their activity may represent the animal’s spatial location for use in memory and navigation. I used simulations to investigate grid cells’ encoding of self-location, showing that some properties of in-vivo firing patterns are adaptive for fidelity. In a related project, I found evidence suggesting medial entorhinal cortex cells may participate in non-local representations of remembered, planned or imagined routes, foreshadowing more recent work. First, I simulated firing patterns in modular grid cell systems with different parameters (e.g. grid scales, orientations), and assessed how well they encode self-location under different conditions (e.g. spatial uncertainty, environment size). I demonstrated that grid cell system parameters affect precision (within the smallest grid scale) and accuracy (including mis-localisation to the wrong repeating unit of a grid) differently. I showed that grid scale expansion partially mitigates the effect of spatial uncertainty on accuracy, supporting the hypothesis that the temporary expansion experimentally observed in rats exploring novel environments may be an adaptive response to uncertainty. In an environment with anisotropic spatial information, I showed that aligning the grid-patterns with the axis in which more information is available improves performance, matching collaborators’ findings that grid-patterns in humans virtually navigating such environments are aligned that way. I showed how self-localisation error in larger environments is influenced by the relation between the modules’ scales. In the presence of spatial uncertainty, absolute predictions of capacity break down, and accuracy varies sharply and irregularly with the ratio between modules’ scales. This, and the observed biological variability of the ratio, make some theoretical predictions of optimised values for the ratio implausible. In sum, I have demonstrated how biologically-inspired simulations can help interpret grid cell firing patterns and explore the adaptiveness of neural coding schemes.
Supervisor: Burgess, N. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available