Conformal Isometry of Lie Group Representation in Recurrent Network of Grid Cells

Understand

The activity of the grid cell population in the medial entorhinal cortex (MEC) of the mammalian brain forms a vector representation of the self-position of the animal.

• Recurrent neural networks have been proposed to explain the properties of the grid cells by updating the neural activity vector based on the velocity input of the animal.
• In doing so, the grid cell system effectively performs path integration.
• In this paper, we investigate the algebraic, geometric, and topological properties of grid cells using recurrent network models.