Place Cells Dynamically Refine Grid Cell Activities To Reduce Error Accumulation During Path Integration in a Continuous Attractor Model

Authors

Jose A Fernandez-Leon
Ahmet Kerim Uysal
Daoyun Ji

Language

English

Publication Date

12-12-2022

Journal

Scientific Reports

DOI

10.1038/s41598-022-25863-2

PMID

36509873

PMCID

PMC9744848

PubMedCentral® Posted Date

12-12-2022

PubMedCentral® Full Text Version

Post-print

Abstract

Navigation is one of the most fundamental skills of animals. During spatial navigation, grid cells in the medial entorhinal cortex process speed and direction of the animal to map the environment. Hippocampal place cells, in turn, encode place using sensory signals and reduce the accumulated error of grid cells for path integration. Although both cell types are part of the path integration system, the dynamic relationship between place and grid cells and the error reduction mechanism is yet to be understood. We implemented a realistic model of grid cells based on a continuous attractor model. The grid cell model was coupled to a place cell model to address their dynamic relationship during a simulated animal's exploration of a square arena. The grid cell model processed the animal's velocity and place field information from place cells. Place cells incorporated salient visual features and proximity information with input from grid cells to define their place fields. Grid cells had similar spatial phases but a diversity of spacings and orientations. To determine the role of place cells in error reduction for path integration, the animal's position estimates were decoded from grid cell activities with and without the place field input. We found that the accumulated error was reduced as place fields emerged during the exploration. Place fields closer to the animal's current location contributed more to the error reduction than remote place fields. Place cells' fields encoding space could function as spatial anchoring signals for precise path integration by grid cells.

Keywords

Animals, Grid Cells, Place Cells, Models, Neurological, Entorhinal Cortex, Orientation, Hippocampus, Action Potentials, Space Perception, Cognitive neuroscience, Computational neuroscience, Network models, Spatial memory

Published Open-Access

yes

Recommended Citation

Jose A Fernandez-Leon, Ahmet Kerim Uysal, and Daoyun Ji, "Place Cells Dynamically Refine Grid Cell Activities To Reduce Error Accumulation During Path Integration in a Continuous Attractor Model" (2022). Faculty and Staff Publications. 4917.
https://digitalcommons.library.tmc.edu/baylor_docs/4917