Neuromorphic Computing and Graph Theory Applications in Information Processing Optimization

This paper explores the intersection of neuromorphic computing and graph theory, emphasizing the role of graph-based models in optimizing neural network structures and enhancing information processing in neuromorphic systems. Neuromorphic computing, inspired by the biological nervous system, presents a paradigm for creating more efficient and adaptable AI systems. The use of graph theory provides a mathematical framework for optimizing network architecture, improving stability, and enabling faster learning processes. By integrating graph-based optimization algorithms, neuromorphic systems can address complex challenges in fields such as robotics, machine learning, and autonomous systems. Experimental results and theoretical models show promising improvements in processing speed and learning adaptability.

کلیدواژه ها:

Neuromorphic Computing ، Graph Theory ، Spiking Neural Networks ، Information Processing ، Network Optimization