Physiological studies reveal that rats rely on multiple spatial cells for spatial navigation and memory. In this paper, we investigated the firing mechanism of spatial cells within the entorhinal-hippocampal structure of the rat brain and proposed a spatial localization model for mobile robot. Its characteristics were as follows: on the basis of the information transmission model from grid cells to place cells, the neural network model of place cells interaction was introduced to obtain the place cell plate with a single-peaked excitatory activity package. Then the solution to the robot’s position was achieved by establishing a transformation relationship between the position of the excitatory activity package on the place cell plate and the robot’s position in the physical environment. In this paper, simulation experiments and physical experiments were designed to verify the model. The experimental results showed that compared with RatSLAM and the model of grid cells to place cells, the positioning performance of the model in this paper was more accurate, and the cumulative error in the long-time path integration process of the robot was also smaller. The research results of this paper lay a foundation for the robot navigation method that mimics the cognitive mechanism of rat brain.
Physiological studies have revealed that rats perform spatial localization relying on grid cells and place cells in the entorhinal-hippocampal CA3 structure. The dynamic connection between the entorhinal-hippocampal structure and the prefrontal cortex is crucial for navigation. Based on these findings, this paper proposes a spatial navigation method based on the entorhinal-hippocampal-prefrontal information transmission circuit of the rat’s brain, with the aim of endowing the mobile robot with strong spatial navigation capability. Using the hippocampal CA3-prefrontal spatial navigation model as a foundation, this paper constructed a dynamic self-organizing model with the hippocampal CA1 place cells as the basic unit to optimize the navigation path. The path information was then fed back to the impulse neural network via hippocampal CA3 place cells and prefrontal cortex action neurons, improving the convergence speed of the model and helping to establish long-term memory of navigation habits. To verify the validity of the method, two-dimensional simulation experiments and three-dimensional simulation robot experiments were designed in this paper. The experimental results showed that the method presented in this paper not only surpassed other algorithms in terms of navigation efficiency and convergence speed, but also exhibited good adaptability to dynamic navigation tasks. Furthermore, our method can be effectively applied to mobile robots.
Place cell with location tuning characteristics play an important role in brain spatial cognition and navigation, but there is relatively little research on place cell screening and its influencing factors. Taking pigeons as model animals, the screening process of pigeon place cell was given by using the spike signal in pigeon hippocampus under free activity. The effects of grid number and filter kernel size on the place field of place cells during the screening process were analyzed. The results from the real and simulation data showed that the proposed place cell screening method presented in this study could effectively screen out place cell, and the research found that the size of place field was basically inversely proportional to the number of grids divided, and was basically proportional to the size of Gaussian filter kernel in the overall trend. This result will not only help to determine the appropriate parameters in the place cell screening process, but also promote the research on the neural mechanism of spatial cognition and navigation of birds such as pigeons.