Neural Dust: An Ultrasonic, Low Power Solution for Chronic Brain-Machine Interfaces
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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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Jul 2013.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 1; 2026: 1.
3 papers.
- 2025A Review of Brain-Computer Interface Technologies: Signal Acquisition Methods and Interaction Paradigmscited by 1
- 2022Bandwidth-efficient distributed neural network architectures with application to body sensor networkscited by 0
- 2026Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computationscited by 0
- 2026Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations12 May 2026
- 2025A Review of Brain-Computer Interface Technologies: Signal Acquisition Methods and Interaction Paradigms1 Mar 2025
- 2022Bandwidth-efficient distributed neural network architectures with application to body sensor networks14 Oct 2022