# Neural Dust: An Ultrasonic, Low Power Solution for Chronic Brain-Machine Interfaces

arXiv: https://arxiv.org/abs/1307.2196 · HTML: https://mlsanity.com/paper/1307.2196/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2013.

## Built on by (most cited first, 3 of 3)

- [A Review of Brain-Computer Interface Technologies: Signal Acquisition Methods and Interaction Paradigms](https://mlsanity.com/paper/2503.16471/) (2025)
- [Bandwidth-efficient distributed neural network architectures with application to body sensor networks](https://mlsanity.com/paper/2210.07750/) (2022)
- [Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations](https://mlsanity.com/paper/2605.15216/) (2026)

## Abstract (arXiv)

A major hurdle in brain-machine interfaces (BMI) is the lack of an implantable neural interface system that remains viable for a lifetime. This paper explores the fundamental system design trade-offs and ultimate size, power, and bandwidth scaling limits of neural recording systems built from low-power CMOS circuitry coupled with ultrasonic power delivery and backscatter communication. In particular, we propose an ultra-miniature as well as extremely compliant system that enables massive scaling in the number of neural recordings from the brain while providing a path towards truly chronic BMI. These goals are achieved via two fundamental technology innovations: 1) thousands of 10 - 100 μ m scale, free-floating, independent sensor nodes, or neural dust, that detect and report local extracellular electrophysiological data, and 2) a sub-cranial interrogator that establishes power and communication links with the neural dust.