Related papers: An all-magnonic neuron with tunable fading memory
Cavity magnonics deals with the interaction of magnons - elementary excitations in magnetic materials - and confined electromagnetic fields. We introduce the basic physics and review the experimental and theoretical progress of this young…
Spiking Neural Networks (SNNs) can do inference with low power consumption due to their spike sparsity. ANN-SNN conversion is an efficient way to achieve deep SNNs by converting well-trained Artificial Neural Networks (ANNs). However, the…
The propagation of magnons along a symmetry path may depend on the direction of propagation, similar to many other quasiparticles in nature. This phenomenon is commonly referred to as nonreciprocity. In addition to the fact that it is of…
Emulating various facets of computing principles of the brain can potentially lead to the development of neuro-computers that are able to exhibit brain-like cognitive capabilities. In this letter, we propose a magnetoelectronic neuron that…
Spiking Neural Networks (SNNs) offer a biologically inspired approach to computer vision that can lead to more efficient processing of visual data with reduced energy consumption. However, maintaining homeostasis within these networks is…
In this work, we present numerical results concerning an integrated photonic non-linear activation function that relies on a power independent, non-linear phase to amplitude conversion in a passive optical resonator. The underlying…
Cavity magnonics investigates hybrid systems where magnons interact coherently with photons, providing a platform to harness light-matter interaction in magnetic materials. Progress in this field hinges on achieving stronger and tunable…
We study a stochastic process describing the continuous time evolution of the membrane potentials of finite system of neurons in the absence of external stimuli. The values of the membrane potentials evolve under the effect of {\it chemical…
Magnonics is a rapidly growing field, attracting much attention for its potential applications in data transport and processing. Many individual magnonic devices have been proposed and realized in laboratories. However, an integrated…
The emerging field of magnonics employs spin waves and their quanta, magnons, to implement wave-based computing on the micro- and nanoscale. Multi-frequency magnon networks would allow for parallel data processing within single logic…
Manipulation of directional magnon propagation, known as magnon spin current, is essential for developing magnonic memory and logic devices featuring nonvolatile functionalities and ultralow power consumption. Magnon spin current can…
Collective magnetic excitation modes, magnons, can be coherently coupled to microwave photons in the single excitation limit. This allows for access to quantum properties of magnons and opens up a range of applications in quantum…
Inspired by the brain, we present a physical alternative to traditional digital neural networks -- a microfluidic network in which nodes are connected by conical, electrolyte-filled channels acting as memristive iontronic synapses. Their…
The field of hybrid magnonics has gained significant momentum in recent years, driven by its potential to enable coherent information transfer and quantum transduction. This study delves into the tunable magnon-magnon coupling within a…
We generalize the concept of triply-degenerate nodal points to non-collinear antiferromagnets. Here, we introduce this concept to insulating quantum antiferromagnets on the decorated honeycomb lattice, with spin-$1$ bosonic quasiparticle…
Monotonic neural networks have recently been proposed as a way to define invertible transformations. These transformations can be combined into powerful autoregressive flows that have been shown to be universal approximators of continuous…
We use micromagnetic simulations to demonstrate how machine learning can be applied to arrays of chiral magnonic resonators to build a magnonic full adder. The chiral magnonic resonators have form of nano-sized permalloy disks that…
Nonlinear dynamics govern a wide array of natural phenomena and are essential for understanding nonequilibrium behaviors in condensed matter systems. In magnetically ordered materials, magnons - the quanta of spin waves - exhibit intrinsic…
Neural networks are composed of neurons and synapses, which are responsible for learning in a slow adaptive dynamical process. Here we experimentally show that neurons act like independent anisotropic multiplex hubs, which relay and mute…
Magnon-polaritons are hybrid light-matter quasiparticles originating from the strong coupling between magnons and photons. They have emerged as a potential candidate for implementing quantum transducers and memories. Owing to the dampings…