# Controllable reset behavior in domain wall-magnetic tunnel junction artificial neurons for task-adaptable computation

arXiv: https://arxiv.org/abs/2101.03095 · HTML: https://mlsanity.com/paper/2101.03095/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2021.

## Built on by (most cited first, 1 of 1)

- [Image Synthesis Using Spintronic Deep Convolutional Generative Adversarial Network](https://mlsanity.com/paper/2601.01441/) (2026)

## Abstract (arXiv)

Neuromorphic computing with spintronic devices has been of interest due to the limitations of CMOS-driven von Neumann computing. Domain wall-magnetic tunnel junction (DW-MTJ) devices have been shown to be able to intrinsically capture biological neuron behavior. Edgy-relaxed behavior, where a frequently firing neuron experiences a lower action potential threshold, may provide additional artificial neuronal functionality when executing repeated tasks. In this study, we demonstrate that this behavior can be implemented in DW-MTJ artificial neurons via three alternative mechanisms: shape anisotropy, magnetic field, and current-driven soft reset. Using micromagnetics and analytical device modeling to classify the Optdigits handwritten digit dataset, we show that edgy-relaxed behavior improves both classification accuracy and classification rate for ordered datasets while sacrificing little to no accuracy for a randomized dataset. This work establishes methods by which artificial spintronic neurons can be flexibly adapted to datasets.