Integrating Mathematical Modeling and Neurobiological Principles in GAN Architectures
Keywords:
Neural Networks, Generative Adversarial Networks, Neurobiological In- spiration, Mathematical Optimization, Artificial Intelligence, Machine Learning, Deep LearningAbstract
Neural networks (NNs) and generative adversarial networks (GANs) are pivotal in advancing artificial intelligence (AI), enabling breakthroughs in image synthesis, natural language processing, and speech generation. Grounded in mathematical optimization and inspired by neurobiological learning mechanisms, these models integrate rigorous computational frameworks with brain-inspired principles. This paper explores how mathematical optimization and neurobiological insights enhance GAN performance, focusing on efficiency, robustness, and generalization. By bridging theoretical rigor with practical applications, we underscore the potential of biologi- cally inspired architectures to develop adaptive and powerful AI systems.
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