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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, beused to construct spiking artificial neurons. When configured as a neuron, themagnetizations in antiferromagnetic materials have an effective inertia thatgives them intrinsic characteristics that closely resemble biological neurons,in contrast with conventional artificial spiking neurons. It is shown here thatantiferromagnetic neurons have a spike duration on the order of a picosecond, apower consumption of about 10^-3 pJ per synaptic operation, and built-infeatures that directly resemble biological neurons, including response latency,refraction, and inhibition. It is also demonstrated that antiferromagneticneurons interconnected into physical neural networks can perform unidirectionaldata processing even for passive symmetrical interconnects. Flexibility ofantiferromagnetic neurons is illustrated by simulations of simple neuromorphiccircuits realizing Boolean logic gates and controllable memory loops.

Artificial neurons based on antiferromagnetic auto-oscillators as a platform for neuromorphic computing