An asynchronous wireless network for capturing event-driven data from large populations of autonomous sensors.
Autor: | Lee J; School of Engineering, Brown University, Providence, RI USA., Lee AH; School of Engineering, Brown University, Providence, RI USA., Leung V; Electrical and Computer Engineering, Baylor University, Waco, TX USA., Laiwalla F; School of Engineering, Brown University, Providence, RI USA., Lopez-Gordo MA; Department of Signal Theory, Telematics and Communications, University of Granada, Granada, Spain., Larson L; School of Engineering, Brown University, Providence, RI USA., Nurmikko A; School of Engineering, Brown University, Providence, RI USA.; Carney Institute for Brain Science, Brown University, Providence, RI USA. |
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Jazyk: | angličtina |
Zdroj: | Nature electronics [Nat Electron] 2024; Vol. 7 (4), pp. 313-324. Date of Electronic Publication: 2024 Mar 19. |
DOI: | 10.1038/s41928-024-01134-y |
Abstrakt: | 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. Competing Interests: Competing interestsThe authors declare no competing interests. (© The Author(s) 2024, corrected publication 2024.) |
Databáze: | MEDLINE |
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