A Framework for Mobile Robot Navigation Using a Temporal Population Code

Recently, we have proposed that the dense local and sparse long-range connectivity of the visual cortex accounts for the rapid and robust transformation of visual stimulus information into a temporal population code, or TPC. In this paper, we combine the canonical cortical computational principle of the TPC model with two other systems: an attention system and a hippocampus model. We evaluate whether the TPC encoding strategy can be efficiently used to generate a spatial representation of the environment. We benchmark our architecture using stimulus input from a real-world environment. We show that the mean correlation of the TPC representation in two different positions of the environment has a direct relationship with the distance between these locations. Furthermore, we show that this representation can lead to the formation of place cells. Our results suggest that TPC can be efficiently used in a high complexity task such as robot navigation.