Open Access
Journal Article
Neuromorphic Computing: Bridging Neuroscience and AI
by
John Harris
as 2023 5(1):37; 10.69610/j.as.20230325 - 25 March 2023
Abstract
Neuromorphic computing represents a groundbreaking intersection of neuroscience and artificial intelligence (AI). This field seeks to emulate the structure and function of the human brain in order to create more efficient and powerful computational systems. By adopting a bottom-up approach, neuromorphic computing explores the principles of neural networks and applies them to ha
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Neuromorphic computing represents a groundbreaking intersection of neuroscience and artificial intelligence (AI). This field seeks to emulate the structure and function of the human brain in order to create more efficient and powerful computational systems. By adopting a bottom-up approach, neuromorphic computing explores the principles of neural networks and applies them to hardware design. The primary aim is to overcome the limitations of traditional von Neumann architectures, which are power-hungry and lack the parallelism found in biological systems. This paper provides an overview of neuromorphic computing, describing its key concepts, architectural designs, and potential applications. We discuss how neuromorphic systems can lead to advancements in cognitive computing, robotics, and real-time processing, while also addressing challenges such as the scalability and energy efficiency of these systems. Furthermore, we delve into the development of neuromorphic hardware, including memristors, neuromorphic chips, and hybrid systems. Ultimately, this paper underscores the significance of neuromorphic computing in bridging the gap between neuroscience and AI, offering a glimpse into the future of computing.