A Computational Model of Grid Cells based on Dendritic Self-organized Learning

Abstract

In this paper we present a new computational model for grid cells. These cells are neurons in the entorhinal cortex of the hippocampal region that encode allocentric spatial information. They possess a peculiar, triangular firing pattern that spans the entire environment with a virtual lattice. We show that such a firing pattern can emerge from a dendritic, self-organized learning process. A key aspect of the proposed model is the hypothesis that the dendritic tree of a grid cell can behave like a sparse self organizing map that tries to cover its input space as best as possible. We argue, that the encoding scheme used by grid cells is possibly not limited to the description of spatial information and may represent a general principle on how complex information is encoded in higher level brain areas like the hippocampal region.