Animals are able to update their knowledge about their current position solely by
integrating the speed and the direction of their movement, which is known as path
integration. Recent discoveries suggest that grid cells in the medial entorhinal cortex
might perform some of the essential underlying computations of path integration. However,
a major concern over path integration is that as the measurement of speed and direction
is inaccurate, the representation of the position will become increasingly unreliable.
In this paper, we study how allothetic inputs can be used to continually correct the
accumulating error in the path integrator system. We set up the model of a mobile
agent equipped with the entorhinal representation of idiothetic (grid cell) and allothetic
(visual cells) information and simulated its place learning in a virtual environment.
Due to competitive learning, a robust hippocampal place code emerges rapidly in the
model. At the same time, the hippocampo-entorhinal feed-back connections are modified
via Hebbian learning in order to allow hippocampal place cells to influence the attractor
dynamics in the entorhinal cortex. We show that the continuous feed-back from the
integrated hippocampal place representation is able to stabilize the grid cell code.
