Brain-Inspired Computing: Bridging Neurobiology and Artificial Intelligence

This paper explores brain-inspired computing as a transformative approach that integrates principles from neurobiology with artificial intelligence (AI) to enhance computational efficiency and adaptability. By mimicking neural structures and cognitive processes, brain-inspired models aim to overcome limitations of traditional AI systems, enabling more robust learning, pattern recognition, and decision-making. The study reviews key neurobiological mechanisms, such as neural plasticity and parallel processing, and discusses their applications in neuromorphic hardware and advanced AI algorithms. This interdisciplinary convergence offers promising pathways for developing intelligent systems that closely emulate human brain function.