# Neural-like computing with populations of superparamagnetic basis functions

arXiv: https://arxiv.org/abs/1610.09394 · HTML: https://mlsanity.com/paper/1610.09394/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2016.

## Built on by (most cited first, 2 of 2)

- [Measurement-driven neural-network training for integrated magnetic tunnel junction arrays](https://mlsanity.com/paper/2312.06446/) (2023)
- [Neuromorphic Spintronics](https://mlsanity.com/paper/2409.10290/) (2024)

## Abstract (arXiv)

In neuroscience, population coding theory demonstrates that neural assemblies can achieve fault-tolerant information processing. Mapped to nanoelectronics, this strategy could allow for reliable computing with scaled-down, noisy, imperfect devices. Doing so requires that the population components form a set of basis functions in terms of their response functions to inputs, offering a physical substrate for calculating. For this purpose, the responses of the nanodevices should be non-linear, and each tuned to different values of the input. These strong requirements have prevented a demonstration of population coding with nanodevices. Here, we show that nanoscale magnetic tunnel junctions can be assembled to meet these requirements. We demonstrate experimentally that a population of nine junctions can implement a basis set of functions, providing the data to achieve, for example, the generation of cursive letters. We design hybrid magnetic-CMOS systems based on interlinked populations of junctions and show that they can learn to realize non-linear variability-resilient transformations with a low imprint area and low power.