Abstract
Mathematical modeling of neural networks in the brain offers a powerful framework for understanding the complexities of cognitive processes, synaptic plasticity, and information processing. These models simulate the dynamics of interconnected neurons and help in exploring emergent phenomena such as learning, memory, and decision-making. By integrating concepts from differential equations, graph theory, and statistical mechanics, researchers can replicate both microscopic and macroscopic neural behaviors. This article presents a comprehensive overview of mathematical techniques used to model brain neural networks, discusses biological interpretations, and emphasizes their relevance in neuroscience and artificial intelligence.
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