Neuromorphic Computing: Emulating the Human Brain for Next-Generation AI
Abstract
Neuromorphic computing aims to bridge the gap between artificial intelligence and biological neural processing by mimicking the brain’s structure and functionality. This paper explores the design and implementation of neuromorphic hardware and spiking neural networks (SNNs) for energy-efficient AI systems. We evaluate their performance on cognitive tasks such as pattern recognition, reinforcement learning, and real-time processing. Comparative analyses highlight advantages in power consumption and adaptability over traditional deep learning models. Challenges in hardware scalability, algorithmic development, and real-world deployment are discussed.
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