Research on hippocampal positioning and navigation model based on energy fields

Place cells in the hippocampus are crucial components of the brain's internal spatial positioning system, involved in constructing cognitive maps of the external environment for animals. However, many existing neuron models that simulate neural activities in the brain require extensive and complex computations. This study presents a place cell neural network model based on the Wang-Zhang model, using a neural energy coding approach. It quantitatively describes the attenuation pattern of place cell cluster firing power and constructs an energy field model. The model employs energy field gradients to address positioning and navigation tasks. Comparative experiments with the Hodgkin-Huxley (HH) model evaluate the navigation efficiency of rodents under different neuron models. The research shows that, compared to the HH model, the Wang-Zhang model has lower computational complexity and higher navigation efficiency. It rapidly constructs and updates cognitive maps, facilitating efficient pathfinding. Additionally, obstacle avoidance and detour experiments are performed using the Wang-Zhang model. Results demonstrate the model's ability for flexible navigation in dynamically changing mazes, validating the Wang-Zhang model and energy coding theory's unique functionality and robust advantages in neural modeling and information processing. This supports the effectiveness of energy coding in spatial memory and path exploration. Moreover, the additive property of neural energy provides significant advantages in neural modeling and computational analysis, offering a viable method for simulating large-scale neural networks and providing a theoretical basis for understanding the neurodynamic mechanisms of spatial memory.