  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.
