International Conference on Information and Knowledge Technology

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

Authors :

Arian Baymani¹ Maryam Naderi Soorki²

1- دانشگاه شهید چمران اهواز 2- دانشگاه شهید چمران اهواز

Keywords :

neural networks،hypergraph theory،electrical circuits،network parameters

Abstract :

To improve and optimize the performance of the nervous system, one can no longer rely solely on traditional one-dimensional analytical approaches from disciplines such as biochemistry or physiology; instead, it is essential to incorporate all aspects of the sciences, including advanced mathematics, electrical engineering, computer engineering, and more. One of the best tools for understanding, increasing accuracy, and enhancing the nervous system's performance is using hypergraph concepts combined with degree n and higher circuits. This approach allows a better understanding of the neural network as a broad and complex functional network. While traditional neural network models have significantly contributed to artificial intelligence, they often need to represent the intricacies inherent to biological neural systems. This limitation has prompted more complex models to capture the dynamic interactions and interconnections between neurons effectively. A hypergraph representation allows for a nuanced and in-depth understanding of neural dynamics by modeling the multifaceted relationships among neurons. By integrating principles from electrical circuit theory into our hypergraph framework, we derive a set of optimization strategies to enhance the functional efficiency and performance of neural networks. This integration enriches the theoretical foundations of our approach and provides practical insights into the operational mechanisms of neural processing. Unlike classical models, the properties of n-degree circuits illustrate the multifaceted functions of neurons and their interconnections, enabling a depiction of how information flows and is processed in the human brain. This paper demonstrates the potential of hypergraph structures to represent neural networks' parallel and sequential processing capabilities. Using higher-order differential equations and their conceptual interpretations, it elucidates the critical role that complex connections play in cognitive functions such as memory, learning, and decision-making. Through simulations and rigorous theoretical analyses, we show that our hypergraph-based method significantly improves the optimization of neural network parameters. The results indicate substantial enhancements in task performance—including pattern recognition, sensory processing, and more complex cognitive functions. We will also explore how incorporating higher-order structures increases the accuracy of neural computations and provides a robust framework for modeling real-world cognitive scenarios that reflect human-like intelligence. This research contributes to the theoretical landscape of neural network optimization and paves the way for future studies to develop more biologically relevant neural models. Ultimately, when integrating engineering, clinical, and medical sciences, all these insights may lead to applications that enhance human-computer interaction and decision-making capabilities.