Learning an efficient place cell map from grid cells using non-negative sparse coding

Abstract

Experimental studies of grid cells in the Medial Entorhinal Cortex (MEC) have shown that they are selective to an array of spatial locations in the environment that form a hexagonal grid. However, place cells in the hippocampus are only selective to a single-location of the environment while granule cells in the dentate gyrus of the hippocampus have multiple discrete firing locations, but lack spatial periodicity. Given the anatomical connection from MEC to the hippocampus, previous feedforward models of grid-to-place have been proposed. Here, we propose a unified learning model that can describe the spatial tuning properties of both hippocampal place cells and dentate gyrus granule cells based on non-negative sparse coding. Sparse coding plays an important role in many cortical areas and is proposed here to have a key role in the navigational system of the brain in the hippocampus.Our results show that the hexagonal patterns of grid cells with various orientations, grid spacings and phases are necessary for model cells to learn a single spatial field that efficiently tile the entire spatial environment. However, if there is a lack of diversity in any grid parameters or a lack of cells in the network, this will lead to the emergence of place cells that have multiple firing locations. More surprisingly, the model shows that place cells can also emerge even when non-negative sparse coding is used with weakly-tuned MEC cells, instead of MEC grid cells, as the input to place cells. This work suggests that sparse coding may be one of the underlying organizing principles for the navigational system of the brain.