NEUROMORPHIC COMPUTING: DESIGNING AI THAT THINKS LIKE THE HUMAN BRAIN

Abstract

Neuromorphic computing is an emerging field that aims to design artificial intelligence systems inspired by the structure and functioning of the human brain. Unlike traditional computing architectures that process information sequentially, neuromorphic systems mimic biological neural networks, enabling highly efficient and parallel data processing. These systems use artificial neurons to learn, adapt, and make decisions with significantly lower energy consumption. Recent advancements in neuromorphic hardware and algorithms have opened new possibilities for next-generation AI systems capable of real-time learning and intelligent decision-making. Neuromorphic chips such as spiking neural networks enable machines to process sensory data more efficiently, making them suitable for robotics, autonomous systems, and edge computing applications. This paper explores the evolution of neuromorphic computing, its applications, major technologies, emerging trends, and challenges in developing brain-inspired AI systems. The study highlights how neuromorphic computing could revolutionize artificial intelligence by making machines more efficient, adaptive, and capable of human-like cognitive processing.