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.
Harris, J. Neuromorphic Computing: Bridging Neuroscience and AI. Advanced Sciences, 2023, 5, 37. https://doi.org/10.69610/j.as.20230325
AMA Style
Harris J. Neuromorphic Computing: Bridging Neuroscience and AI. Advanced Sciences; 2023, 5(1):37. https://doi.org/10.69610/j.as.20230325
Chicago/Turabian Style
Harris, John 2023. "Neuromorphic Computing: Bridging Neuroscience and AI" Advanced Sciences 5, no.1:37. https://doi.org/10.69610/j.as.20230325
APA style
Harris, J. (2023). Neuromorphic Computing: Bridging Neuroscience and AI. Advanced Sciences, 5(1), 37. https://doi.org/10.69610/j.as.20230325
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