Asynchronous Large-Scale Networks for Spatially Distributed Wireless RF Event Sensors

Abstract We describe a wireless RF network for capturing event-driven data from thousands of spatially distributed sensors. As asynchronous devices, each sensor detects events within its local environment. Information acquired by the full network can enable prediction of the time evolution of the system, whether a brain or cardiac circuit in the human body, or an assistive living environment, for example. We develop a communication concept inspired by principles of synaptic information processing in the brain which we mimic by a code-division multiple access strategy in a sparse network. Through extensive simulation, we optimize wireless transmission from ensembles of event-detecting sensors for efficient use of the power and spectrum at low error rates, which is then implemented on-chip to demonstrate the core communication scheme in silico. We also apply the concept to recordings from thirty thousand neurons in the primate cortex, to decode and predict forward state trajectories for hand movement.