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.

空间分布的射频识别传感器网络可用于收集可穿戴或可植入生物医学应用中的数据。然而，可扩展网络的开发仍然具有挑战性。在这里，我们报告了一种无线射频网络方法，可以从大量空间分布的自主微传感器中捕获稀疏事件驱动的数据。我们使用基于码分多址方法的频谱高效、低错误率异步网络概念。我们通过实验证明了几十个亚毫米尺寸硅微芯片的网络性能，并通过大规模硅片模拟对其进行了补充。为了测试基于尖峰的无线通信可以通过神经拟态计算技术与下游传感器群体分析相匹配的概念，我们使用尖峰神经网络机器学习模型来解码来自灵长类动物皮层中的八千个尖峰神经元的预先记录的开源数据，以进行准确的预测光标控制任务中的手部移动。