An Entorhinal-Hippocampal Model for Simultaneous Cognitive Map Building

Hippocampal place cells and entorhinal grid cells have been hypothesized to be able to form map-like spa-tial representation of the environment, namely cogni-tive map. In most prior approaches, either neural net-work methods or only hippocampal models are used for building cognitive maps, lacking biological fidelity to the entorhinal-hippocampal system. This paper presents a novel computational model to build cognitive maps of real environments using both place cells and grid cells. The proposed model includes two major components: (1) A competitive Hebbian learning algorithm is used to select velocity-coupled grid cell population activi-ties, which path-integrate self-motion signals to deter-mine computation of place cell population activities; (2) Visual cues of environments are used to correct the ac-cumulative errors intrinsically associated with the path integration process. Experiments performed on a mobile robot show that cognitive maps of the real environment can be efficiently built. The proposed model would pro-vide an alternative neuro-inspired approach for robotic mapping, navigation and localization