Learning spatio-temporal properties of hippocampal place cells

Hippocampal place cells have spatio-temporal properties: they generally respond to a single spatial location of a small environment; in addition, they also display the temporal response property of theta phase precession, namely that the phase of spiking relative to the theta wave shifts from the late phase to early phase as the animal crosses the place field. Grid cells in layer II of the medial entorhinal cortex (MEC) also have spatio-temporal properties similar to hippocampal place cells, except that grid cells respond to multiple spatial locations that form a hexagonal pattern. Other non-grid spatial cells are also abundant in the entorhinal cortex (EC). Because the EC is the upstream area that projects strongly to the hippocampus, a number of EC-hippocampus models have been proposed to explain how the spatial receptive field properties of place cells emerge. However, none of these learning models have explained how the temporal response properties of hippocampal place cells emerge as a result of the EC input. A learning model is presented here based on non-negative sparse coding in which we show that the spatial and temporal properties of hippocampal place cells can be simultaneously learnt from EC input: both MEC grid cells and other EC spatial cells contribute to the spatial properties of hippocampal place cells while MEC grid cells predominantly determine the temporal response properties of hippocampal place cells.