The Advances in Neuromorphic Computing and Brain-Inspired Systems (ANCBIS).

Resumen

Neuromorphic computing, inspired by the structure and functions of the human brain, is transforming the development of energy-efficient, adaptive, and highly parallel processing systems. This field seeks to bridge the gap between traditional computing architectures and biological neural networks by replicating brain-like functionalities. This paper examines recent advancements in neuromorphic computing, with an emphasis on innovative hardware and algorithms that boost computational power while reducing energy consumption. Key technologies such as memristive devices, spiking neural networks, and brain-inspired learning algorithms show promise in applications like pattern recognition, sensory processing, and autonomous decision-making. This study also addresses challenges related to scalability, robustness, and integration with existing systems, emphasizing the importance of cross-disciplinary collaboration to overcome these limitations. By exploring applications in robotics, medical diagnostics, and environmental monitoring, this research highlights how brain-inspired systems could drive the next generation of artificial intelligence and sustainable computing, meeting the growing need for energy-efficient, intelligent systems.