Periodic structure of memory function in spintronics reservoir with feedback current

Abstract

The role of the feedback effect on physical reservoir computing is studied theoretically by solving the vortex-core dynamics in a nanostructured ferromagnet. Although the spin-transfer torque due to the feedback current makes the vortex dynamics complex, it is clarified that the feedback effect does not always contribute to the enhancement of the memory function in a physical reservoir. The memory function, characterized by the correlation coefficient between the input data and the dynamical response of the vortex core, becomes large when the delay time of the feedback current is not an integral multiple of the pulse width. On the other hand, the memory function remains small when the delay time is an integral multiple of the pulse width. As a result, a periodic behavior for the short-term memory capacity is observed with respect to the delay time, the phenomenon of which can be attributed to correlations between the virtual neurons via the feedback current.

10 pages, 9 figures

References in corpus (18)

• Vowel recognition with four coupled spin-torque nano-oscillators
• Currents, Torques, and Polarization Factors in Magnetic Tunnel Junctions
• Physical reservoir computing -- An introductory perspective
• Harnessing disordered quantum dynamics for machine learning
• Magnetic Vortex Core Dynamics in a Ferromagnetic Dot
• Potential implementation of Reservoir Computing models based on magnetic skyrmions
• Vortex oscillations induced by a spin-polarized current in a magnetic nanopillar: Evidence for a failure of the Thiele approach
• Boosting computational power through spatial multiplexing in quantum reservoir computing
• Macromagnetic simulation for reservoir computing utilizing spin dynamics in magnetic tunnel junctions
• Reservoir computing with the frequency, phase and amplitude of spin-torque nano-oscillators
• Low-frequency vortex dynamic susceptibility and relaxation in mesoscopic ferromagnetic dots
• Energy-efficient stochastic computing with superparamagnetic tunnel junctions
• Temporal pattern recognition with delayed feedback spin-torque nano-oscillators
• Optimal modularity and memory capacity of neural reservoirs
• Reservoir computing with dipole-coupled nanomagnets
• Chaos in nanomagnet via feedback current
• Pattern generation and symbolic dynamics in a nanocontact vortex oscillator
• Overcoming device unreliability with continuous learning in a population coding based computing system

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• Non-periodic input-driven magnetization dynamics in voltage-controlled parametric oscillator
• A magnetic domain wall Mackey-Glass oscillator
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• Phase locking in voltage-controlled parametric oscillator
• Associative memory by virtual oscillator network based on single spin-torque oscillator
• Echo state property and memory capacity of artificial spin ice
• Quantum Transport Reservoir Computing