@misc{10481/95412,
year = {2024},
month = {3},
url = {https://hdl.handle.net/10481/95412},
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.},
organization = {NIH Award 1S10OD025181 (Brown University for
computational resources)},
publisher = {Nature Research},
title = {An asynchronous wireless network for capturing event-driven data from large populations of autonomous sensors Jihun Lee},
doi = {10.1038/s41928-024-01134-y},
author = {Lee, Jihun and Lee, Ah-Hyoung and Leung, Vincent and Laiwalla, Farah and López Gordo, Miguel Ángel and Larson, Lawrence and Nurmikko, Arto},
}