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An asynchronous wireless network for capturing event-driven data from large populations of autonomous sensors

Nature Electronics volume 7, pages 313–324 (2024)Cite this article

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Abstract

Networks of spatially distributed radiofrequency identification sensors could be used to collect data in wearable or implantable biomedical applications. However, the development of scalable networks remains challenging. Here we report a wireless radiofrequency network approach that can capture sparse event-driven data from large populations of spatially distributed autonomous microsensors. We use a spectrally efficient, low-error-rate asynchronous networking concept based on a code-division multiple-access method. We experimentally demonstrate the network performance of several dozen submillimetre-sized silicon microchips and complement this with large-scale in silico simulations. To test the notion that spike-based wireless communication can be matched with downstream sensor population analysis by neuromorphic computing techniques, we use a spiking neural network machine learning model to decode prerecorded open source data from eight thousand spiking neurons in the primate cortex for accurate prediction of hand movement in a cursor control task.

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Fig. 1: ASBIT-based communications system overview.
Fig. 2: Prototype of ASBIT system-on-chip wireless ASIC with on-chip oscillator, coil antenna and digital logic circuit.
Fig. 3: An improvement of EER and network capacity for microchips with clock frequency divider.
Fig. 4: Transmitting and decoding over 8,000 channels of spiking neural data.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

Custom-developed MATLAB code for RF simulation and demodulation for the ASBIT protocol is available in the Supplementary Information and from the corresponding author upon reasonable request.

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Acknowledgements

We acknowledge N. Fathy, J. Huang, S. Li, S. Yu, L. Cui, S. Alluri and M. Lokhandwala at UCSD for their previous work on ASIC backbone design. We also thank Y.-K. Song and D. Durfee for their insights into ASIC and hardware design and T. Shea and M. Davies at Intel Labs for sharing their expertise of SNN technologies. We have greatly benefited from the insight of our colleagues across multiple fields from microelectronics to brain sciences and clinical neurology: P. Asbeck, J. Donoghue, B. Dutta, J. Groe, L. Hochberg, T. Sejnowsky, K. Shenoy and S. Cash, among others. This research was supported by private gifts and NIH Award 1S10OD025181 (Brown University for computational resources).

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Author notes

  1. These authors contributed equally: Jihun Lee, Ah-Hyoung Lee.

Authors and Affiliations

  1. School of Engineering, Brown University, Providence, RI, USA

    Jihun Lee, Ah-Hyoung Lee, Farah Laiwalla, Lawrence Larson & Arto Nurmikko

  2. Electrical and Computer Engineering, Baylor University, Waco, TX, USA

    Vincent Leung

  3. Department of Signal Theory, Telematics and Communications, University of Granada, Granada, Spain

    Miguel Angel Lopez-Gordo

  4. Carney Institute for Brain Science, Brown University, Providence, RI, USA

    Arto Nurmikko

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Contributions

A.N. conceived the project. A.N. and J.L. designed the ASBIT wireless data communication method. J.L. and A.-H.L. designed the wireless ASICs and performed the experiments. J.L., A.-H.L. and A.N. wrote the paper. All authors contributed to the data analysis and provided feedback on the manuscript.

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Correspondence to Arto Nurmikko.

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Supplementary Figs. 1–9, Notes 1–6 and Tables 1 and 2.

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Lee, J., Lee, AH., Leung, V. et al. An asynchronous wireless network for capturing event-driven data from large populations of autonomous sensors. Nat Electron 7, 313–324 (2024). https://doi.org/10.1038/s41928-024-01134-y

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