Theoretical and Computational Neuroscience
Author: Luca Sarramone | Email: lsarramone@intia.exa.unicen.edu.ar
Luca Sarramone1°2°, Jose A. Fernandez-Leon1°2°3°
1° Exactas-INTIA, Universidad Nacional del Centro de la Provincia de Buenos Aires (UNCPBA), Tandil, Buenos Aires, Argentina
2° Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
3° CIFICEN (CONICET–CICPBA-UNCPBA), CCT-Tandil, Buenos Aires, Argentina
Grid cells in the medial entorhinal cortex (MEC) are organized in different modules that sustain the mammal’s self-localization system. Recent evidence shows that grid modules remain coordinated during animal foraging. However, the mechanism behind this coordination and its role in spatial navigation are still unclear. We hypothesized that a shared velocity input among all grid modules could explain such coordination. By using a continuous attractor model of grid cells, we tested whether a simulated e-puck robot can self-estimate its position on each time step relative to a starting point when exploring a square arena. Visited places were encoded as place fields and used to reset the current estimated position for error reduction. The impact of grid module coordination on spatial navigation was evaluated by guiding the robot to a goal based solely on the grid modules’ activity. When grid cell activity was reset to the absolute coordinates of the place fields, the position prediction error was significantly reduced. Contrarily, when the reset happened relative to the place fields’ estimated position, the prediction error significantly increased. Grid modules remained coordinated either when noise was added to the input velocity signal, or the estimated position deviated from the actual one. Together, this work suggests that grid module coordination enables navigation to a goal position even when the estimated position is imprecise.