Abstract
Brain-inspired artificial intelligence (AI) is a rapidly evolving field that seeks to model computational systems after the structure, processes, and functioning of the human brain. By drawing from neuroscience and cognitive science, brain-inspired AI aims to improve the efficiency, scalability, and adaptability of machine learning algorithms. This paper explores the key technologies and advancements in the realm of brain-inspired AI, including neural networks, neuromorphic hardware, brain-computer interfaces, and algorithms inspired by biological learning mechanisms. Additionally, we will analyze the challenges and future opportunities in achieving more brain-like cognitive systems. The integration of these technologies promises a paradigm shift in AI research, bringing us closer to artificial general intelligence (AGI) while creating more energy-efficient and resilient systems. Keywords Brain-inspired AI, Neural Networks, Neuromorphic Computing, Spiking Neural Networks, Artificial General Intelligence, Brain-Computer Interfaces, Cognitive Architectures.
Published Version
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