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

TL;DR

This paper presents a novel asynchronous wireless RF network for large-scale event-driven sensor data collection, inspired by brain processing, with experimental validation and application to neuromorphic decoding of neural signals.

Contribution

It introduces a spectrally efficient, low-error asynchronous networking method for large sensor populations, combining experimental microchip characterization with neuromorphic decoding techniques.

Findings

Network achieves low error rates in experiments

Successful decoding of neural signals for cursor control

Scalable to thousands of sensors

Abstract

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.