{"id":575,"date":"2023-10-03T15:45:10","date_gmt":"2023-10-03T19:45:10","guid":{"rendered":"https:\/\/sciences.ucf.edu\/physics\/afmspin\/?p=575"},"modified":"2023-10-03T15:45:10","modified_gmt":"2023-10-03T19:45:10","slug":"artificial-neurons-based-on-antiferromagnetic-auto-oscillators-as-a-platform-for-neuromorphic-computing","status":"publish","type":"post","link":"https:\/\/sciences.ucf.edu\/physics\/afmspin\/artificial-neurons-based-on-antiferromagnetic-auto-oscillators-as-a-platform-for-neuromorphic-computing\/","title":{"rendered":"Artificial neurons based on antiferromagnetic auto-oscillators as a platform for neuromorphic computing"},"content":{"rendered":"

\"\"<\/p>\n

Spiking artificial neurons emulate the voltage spikes of biological neurons and constitute the building blocks of a new class of energy efficient, neuromorphic computing systems. Antiferromagnetic materials can, in theory, be used to construct spiking artificial neurons. When configured as a neuron, the magnetization in antiferromagnetic materials has an effective inertia that gives them intrinsic characteristics that closely resemble biological neurons, in contrast with conventional artificial spiking neurons. It is shown here that antiferromagnetic neurons have a spike duration on the order of picoseconds, a power consumption of about 10\u22123 pJ per synaptic operation, and built-in features that directly resemble biological neurons, including response latency, refraction, and inhibition. It is also demonstrated that antiferromagnetic neurons interconnected into physical neural networks can perform unidirectional data processing even for passive symmetrical interconnects. The flexibility of antiferromagnetic neurons is illustrated by simulations of simple neuromorphic circuits realizing Boolean logic gates and controllable memory loops.<\/p>\n

This article was published in AIP Advances and is available here<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Spiking artificial neurons emulate the voltage spikes of biological neurons and constitute the building blocks of a new class of energy efficient, neuromorphic computing systems. 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Antiferromagnetic materials can, in theory, Continue Reading →\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sciences.ucf.edu\/physics\/afmspin\/artificial-neurons-based-on-antiferromagnetic-auto-oscillators-as-a-platform-for-neuromorphic-computing\/\" \/>\n<meta property=\"article:published_time\" content=\"2023-10-03T19:45:10+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sciences.ucf.edu\/physics\/afmspin\/wp-content\/uploads\/sites\/31\/2023\/10\/Picture-7-248x300.jpeg\" \/>\n<meta name=\"author\" content=\"Gregory Fritjofson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Gregory Fritjofson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" 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