Electrophysiological Modeling and Interactive Approaches of Electrical Circuits and Hypergraphs for Understanding Neural Circuit Dynamics

To enhance the nervous system's performance, it is essential to move beyond traditional analytical methods from biochemistry and physiology and incorporate advanced mathematics, electrical engineering, and computer engineering. Utilizing hypergraph concepts and higher-degree circuits allows for a better understanding of neural networks as complex systems and reflects the dynamic interactions of neurons. This approach provides detailed insights into neural dynamics and develops optimization strategies to improve network efficiency. The study emphasizes the capacity of hypergraph structures to capture both parallel and sequential neural processing. It highlights the role of complex connections in cognitive functions like memory and decision-making. Simulations suggest that this hypergraph-based method significantly optimizes neural network parameters, enhancing performance in tasks such as pattern recognition. Ultimately, these insights could lead to more biologically relevant models and improve human-computer interaction and decision-making in various fields.