Abstract
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.
Highlights
Artificial intelligence technology is regarded as the core technology of the new technology revolution
According to the above analysis, the generation process of a grid cell population that can respond to self-motion information can be divided into three steps: x discrete inhibitory grid cells located in the same module are generated from the place cell population, y the continuous attractor network (CAN) model of the grid cell population is constructed based on an autoencoder network and convolution kernels, and z based on the response of excitatory grid cells to self-motion information and the asymmetric weights from inhibitory grid cells to excitatory grid cells, the grid cell can generate a hexagonal firing field depending on only selfmotion information
THE CONTINUOUS ATTRACTOR NETWORK MODEL OF THE GRID CELL POPULATION WITH SELF-MOTION INFORMATION RESPONSE CAPABILITY On the basis of the CAN model of the grid cell population obtained in section III.B, the response of the excitatory grid cells to self-motion information, and the asymmetric weights from the inhibitory grid cells to the excitatory grid cells are used to make the firing rates of the grid cells change with the movement of the agent
Summary
Artificial intelligence technology is regarded as the core technology of the new technology revolution. The hippocampal structure is an important part of the limbic system in the brain. It incorporates a variety of neurons related to episodic memory [2], [3], environmental representation [4], [5], and spatial navigation [6], [7], such as place cells [8], grid cells [9], head-direction cells [10], and boundary cells [11]. Place cells and grid cells are located in different regions of the hippocampal structure and represent the environment in different ways.
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