A Brain-Inspired Adaptive Space Representation Model Based on Grid Cells and Place Cells

Abstract EN

Grid cells and place cells are important neurons in the animal brain.

The information transmission between them provides the basis for the spatial representation and navigation of animals and also provides reference for the research on the autonomous navigation mechanism of intelligent agents.

Grid cells are important information source of place cells.

The supervised learning and unsupervised learning models can be used to simulate the generation of place cells from grid cell inputs.

However, the existing models preset the firing characteristics of grid cell.

In this paper, we propose a united generation model of grid cells and place cells.

First, the visual place cells with nonuniform distribution generate the visual grid cells with regional firing field through feedforward network.

Second, the visual grid cells and the self-motion information generate the united grid cells whose firing fields extend to the whole space through genetic algorithm.

Finally, the visual place cells and the united grid cells generate the united place cells with uniform distribution through supervised fuzzy adaptive resonance theory (ART) network.

Simulation results show that this model has stronger environmental adaptability and can provide reference for the research on spatial representation model and brain-inspired navigation mechanism of intelligent agents under the condition of nonuniform environmental information.