2020/03/31 by Shu Zhang, Yaroslav Tserkovnyak · 19 citations
Computer Science · Engineering · Mathematics · Physics and Astronomy · #Advanced Memory and Neural Computing #Antiferromagnetism #Artificial intelligence #Artificial neural network #Computer science #Condensed matter physics #Consistency (knowledge bases) #Electrical engineering #Engineering #Ferromagnetism #Magnetic properties of thin films #Mathematics #Neural Networks and Reservoir Computing #Neuromorphic engineering #Physics #Realization (probability) #Spike (software development) #Spiking neural network #Spin (aerodynamics) #Spintronics #Topology (electrical circuits) #cond-mat.mes-hall #physics.app-ph
paper · pdf · doi:10.1103/physrevlett.125.207202
published in Physical Review Letters 125(20), 207202 (American Physical Society) · 7 pages including the supplemental material, 3 figures, published version
openalex publication_date 2020/11/12 · arxiv created 2020/11/13 · arxiv updated 2020/11/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.