Related papers: Shape-based Magnetic Domain Wall Drift for an Arti…
The leaky integrate and fire (LIF) neuron represents standard neuronal model used for numerical simulations. The leakage is implemented in the model as exponential decay of trans-membrane voltage towards its resting value. This makes…
The propagation of a head-to-head magnetic domain-wall (DW) or a tail-to-tail DW in a magnetic nanowire under a static field along the wire axis is studied. Relationship between the DW velocity and DW structure is obtained from the energy…
Dynamics of magnetization domain walls (DWs) in thin ferromagnetic nanotubes subject to longitudinal external fields is addressed analytically in the regimes of strong and weak penalization. Explicit functional forms of the DW profiles and…
We represent the results of micromagnetic modeling and experimental magnetic force microscopy investigations of domain wall (DW) pinning-depinning effects in the hybrid systems consisting of ferromagnetic nanowire (NW) and two ferromagnetic…
We investigate experimentally the effects of externally applied strain on the injection of 180$^\circ$ domain walls (DW) from a nucleation pad into magnetic nanowires, as typically used for DW-based sensors. In our study the strain,…
A thory of field-induced domain wall (DW) propagation is developed. The theory not only explains why a DW in a defect-free nanowire must propagate at a finite velocity, but also provides a proper definition of DW propagation velocity. This…
Spiking neural networks (SNNs), inspired by the neural circuits of the brain, are promising in achieving high computational efficiency with biological fidelity. Nevertheless, it is quite difficult to optimize SNNs because the functional…
Non-Boolean computing based on emerging post-CMOS technologies can potentially pave the way for low-power neural computing platforms. However, existing work on such emerging neuromorphic architectures have either focused on solely mimicking…
Spin waves (or magnons) interact with magnetic domain walls (DWs) in a complicated way that a DW can propagate either along or against magnon flow. However, thermally activated magnons always drive a DW to the hotter region of a nanowire of…
We predict a fast domain wall (DW) motion induced by a thermal gradient across a nanoscopic ferromagnetic stripe of MnBi. The driving mechanism is an exchange torque fueled by magnon accumulation at the DWs. Depending on the thickness of…
Spintronic devices have been widely studied for the hardware realization of artificial neurons. The stochastic switching of magnetic tunnel junction driven by the spin torque is commonly used to produce the sigmoid activation function.…
We theoretically study the interaction of spin waves and a domain wall in a quasi-one-dimensional easy-cone ferromagnet. The gapless spin waves on top of a domain wall are found to exhibit finite reflection in contrast to the well-known…
Nonlinear interactions in the dendritic tree play a key role in neural computation. Nevertheless, modeling frameworks aimed at the construction of large-scale, functional spiking neural networks, such as the Neural Engineering Framework,…
The increasing complexity and energy demands of large-scale neural networks, such as Deep Neural Networks (DNNs) and Large Language Models (LLMs), challenge their practical deployment in edge applications due to high power consumption, area…
Spiking neural networks (SNNs) are posited as a computationally efficient and biologically plausible alternative to conventional neural architectures, with their core computational framework primarily using the leaky integrate-and-fire…
Control of magnetic domain wall motion by electric fields has recently attracted scientific attention because of its potential for magnetic logic and memory devices. Here, we report on a new driving mechanism that allows for magnetic domain…
Up to now, modern Machine Learning is mainly based on fitting high dimensional functions to enormous data sets, taking advantage of huge hardware resources. We show that biologically inspired neuron models such as the…
The ability to rapidly manipulate domain walls (DWs) in magnetic materials is key to developing novel high-speed spintronic memory and computing devices. Antiferromagnetic (AFM) materials present a particularly promising platform due to…
We study in this paper the effect of an unique initial stimulation on random recurrent networks of leaky integrate and fire neurons. Indeed given a stochastic connectivity this so-called spontaneous mode exhibits various non trivial…
Sliding ferroelectrics constructed from stacked nonpolar monolayers enable out-of-plane polarization in two dimensions with exceptional properties, including ultrafast switching speeds and fatigue-free behavior. However, the widely accepted…