An Entorhinal-Hippocampal Model for Simultaneous Cognitive Map Building


  • Miaolong Yuan Institute for Infocomm Research
  • Bo Tian Institute for Infocomm Research
  • Vui Ann Shim Institute for Infocomm Research
  • Huajin Tang Institute for Infocomm Research
  • Haizhou Li Institute for Infocomm Research



cognitive map, robotic spatial cognition, brain-inspired SLAM


Hippocampal place cells and entorhinal grid cells have been hypothesized to be able to form map-like spatial representation of the environment, namely cognitive map. In most prior approaches, either neural network 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 activities, which path-integrate self-motion signals to determine computation of place cell population activities; (2) Visual cues of environments are used to correct the accumulative 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 provide an alternative neuro-inspired approach for robotic mapping, navigation and localization.




How to Cite

Yuan, M., Tian, B., Shim, V. A., Tang, H., & Li, H. (2015). An Entorhinal-Hippocampal Model for Simultaneous Cognitive Map Building. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1).



AAAI Technical Track: Cognitive Systems