NEURO-AI CONVERGENCE: BRIDGING THE GAP BETWEEN NEUROSCIENCE AND ARTIFICIAL INTELLIGENCE
Keywords:
Neuro-AI Convergence, Brain-Inspired Computing, Neuromorphic Systems, Cognitive Modeling, Brain-Computer InterfacesAbstract
This comprehensive article explores the burgeoning field of neuro-AI convergence, examining the intricate relationship between neuroscience and artificial intelligence. The article traces the historical context of this interdisciplinary domain, highlighting key milestones that have led to the current synergy between brain science and machine learning. It delves into how neuroscientific insights have informed AI development, particularly in neural network architectures, learning mechanisms, and memory systems. Conversely, the article discusses the significant contributions of AI to neuroscience, including advanced computational modeling of brain functions, sophisticated data analysis techniques for neuroimaging, and cutting-edge brain-computer interfaces. The article also addresses the field's critical challenges, such as the persistent differences between biological and artificial neural networks, ethical considerations, and technological constraints. The article explores emerging research areas, potential applications in healthcare and cognitive enhancement, and the profound implications for our understanding of consciousness and cognition. By synthesizing current knowledge and pointing toward future directions, this review underscores the transformative potential of neuro-AI convergence in revolutionizing our understanding of the brain and the development of intelligent systems.
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