Reservoir Computing is a type of recursive neural network commonly used for recognizing and predicting spatio-temporal events relying on a complex hierarchy of nested feedback loops to generate a memory functionality. The Reservoir Computing paradigm does not require any knowledge of the reservoir topology or node weights for training purposes and can therefore utilize naturally existing networks formed by a wide variety of physical processes. Most efforts prior to this have focused on utilizing memristor techniques to implement recursive neural networks. This paper examines the potential of skyrmion fabrics formed in magnets with broken inversion symmetry that may provide an attractive physical instantiation for Reservoir Computing.
@article{arxiv.1709.08911,
title = {Potential implementation of Reservoir Computing models based on magnetic skyrmions},
author = {George Bourianoff and Daniele Pinna and Matthias Sitte and Karin Everschor-Sitte},
journal= {arXiv preprint arXiv:1709.08911},
year = {2018}
}