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Grid cells are important neurons related to spatial cognition and navigation in the animal brain and can respond to both external information and self-motion information. The existing models simulated these two types of responses separately, but how to simultaneously develop the two types of connection to respond to the different information has not been simulated. In this paper, first, we develop the connections from place cells to grid cells through improved nonnegative principal component analysis. Then, we construct a continuous attractor network model and an autoencoder network model of grid cell module and convert the parameter learning in the continuous attractor network into weight learning in the autoencoder network. Through parameter learning, the continuous attractor network model can spontaneously generate a hexagonal firing pattern. Ultimately, the grid cell firing fields driven by place cell inputs and self-motion information have the same spacing and direction, which means that the grid cell module can respond the same to these two types of information. This model can provide a reference for the construction of an unmanned agent brain-inspired navigation system.

The Self-Motion Information Response Model in Brain-Inspired Navigation