Learning Orientations: a Discrete Geometry Model

Abstract

In the mammalian brain, many neuronal ensembles are involved in representing spatial structure of the environment. In particular, there exist cells that encode the animals location and cells that encode head direction. A number of studies have addressed properties of the spatial maps produced by these two populations of neurons, mainly by establishing correlations between their spiking parameters and geometric characteristics of the animals environments. The question remains however, how the brain may intrinsically combine the direction and the location information into a unified spatial framework that enables animals orientation. Below we propose a model of such a framework, using ideas and constructs from algebraic topology and synthetic affine geometry.