# An Asynchronous Wireless Network for Capturing Event-Driven Data from Large Populations of Autonomous Sensors

arXiv: https://arxiv.org/abs/2305.12293 · HTML: https://mlsanity.com/paper/2305.12293/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2023.

## Built on by (most cited first, 5 of 5)

- [Neuromorphic Wireless Split Computing with Multi-Level Spikes](https://mlsanity.com/paper/2411.04728/) (2024)
- [Event-Driven Imaging in Turbid Media: A Confluence of Optoelectronics and Neuromorphic Computation](https://mlsanity.com/paper/2309.06652/) (2023)
- [Neuromorphic Optical Tracking and Imaging of Randomly Moving Targets through Strongly Scattering Media](https://mlsanity.com/paper/2501.03874/) (2025)
- [Online Reliable Anomaly Detection via Neuromorphic Sensing and Communications](https://mlsanity.com/paper/2510.14688/) (2025)
- [Neuromorphic Decoding and Dynamical Image Reconstruction from Populations of Spiking Retinal Ganglion Cells](https://mlsanity.com/paper/2610.17154/) (2026)

## Abstract (arXiv)

We introduce a wireless RF network concept for capturing sparse event-driven data from large populations of spatially distributed autonomous microsensors, possibly numbered in the thousands. Each sensor is assumed to be a microchip capable of event detection in transforming time-varying inputs to spike trains. Inspired by brain information processing, we have developed a spectrally efficient, low-error rate asynchronous networking concept based on a code-division multiple access method. We characterize the network performance of several dozen submillimeter-size silicon microchips experimentally, complemented by larger scale in silico simulations. A comparison is made between different implementations of on-chip clocks. Testing the notion that spike-based wireless communication is naturally matched with downstream sensor population analysis by neuromorphic computing techniques, we then deploy a spiking neural network (SNN) machine learning model to decode data from eight thousand spiking neurons in the primate cortex for accurate prediction of hand movement in a cursor control task.