Antiferromagnet-Based Neuromorphics Using Dynamics of Topological Charges

Abstract

We propose a spintronics-based hardware implementation of neuromorphic computing, specifically, the spiking neural network, using topological winding textures in one-dimensional antiferromagnets. The consistency of such a network is emphasized in light of the conservation of topological charges, and the natural spatiotemporal interconversions of magnetic winding. We discuss the realization of the leaky integrate-and-fire behavior of neurons and the spike-timing-dependent plasticity of synapses. Our proposal opens the possibility for an all-spin neuromorphic platform based on antiferromagnetic insulators.

arXiv comment7 pages including the supplemental material, 3 figures, published version

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Cited by in corpus (7)

• Quantum materials for energy-efficient neuromorphic computing
• Physical Neural Networks with Self-Learning Capabilities
• Antiferromagnetic droplet soliton driven by spin current
• First-principles Prediction of Potential Candidate Materials MCu${}_3$X${}_4$ (M = V, Nb, Ta; X = S, Se, Te) for Neuromorphic Computing
• Topological transport of vorticity on curved magnetic membranes
• Artificial neurons based on antiferromagnetic auto-oscillators as a platform for neuromorphic computing
• A proposal for leaky integrate-and-fire neurons by domain walls in antiferromagnetic insulators