Asynchronous Wireless Network for Transmission of Neural Data from Thousands of Autonomous Microimplants

We introduce a large-scale wireless RF network concept for capturing neural data from spatially distributed autonomous microchip implants. Each sensor chip is assumed to be capable of event detection by transforming time-varying input signals to spike trains. Inspired by the brain’s information processing, we have developed a spectrally efficient, low-error rate asynchronous networking protocol based on a code-division multiple access method. We test the network performance with dozens of sub-millimeter-size silicon microchips in situ on laboratory testbench, complemented by large scale in silico simulations. Through an application example relevant to brain-computer interfaces, we simulate the wireless transmission of spiking data from eight thousand neurons in the primate cortex for accurate hand movement prediction in a cursor control task.