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A model of grid cells based on a path integration mechanism

by Alexis Guanella, Paul F.M.J. Verschure
Artificial Neural Networks–ICANN 2006 ()

Abstract

The grid cells of the dorsocaudal medial entorhinal cortex (dMEC) in rats show higher firing rates when the position of the animal correlates with the vertices of regular triangular tessellations covering the environment. Strong evidence indicates that these neurons are part of a path integration system. This raises the question, how such a system could be implemented in the brain. Here, we present a cyclically connected artificial neural network based on a path integration mechanism, implementing grid cells on a simulated mobile agent. Our results show that the synaptic connectivity of the network, which can be represented by a twisted torus, allows the generation of regular triangular grids across the environment. These tessellations share same spacing and orientation, as neighboring grid cells in the dMEC. A simple gain and bias mechanism allows to control the spacing and the orientation of the grids, which suggests that these different characteristics can be generated by a unique algorithm in the brain.

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