  We present the Surrogate-gradient Online Error-triggered Learning (SOEL)
system for online few-shot learning on neuromorphic processors. The SOEL
learning system uses a combination of transfer learning and principles of
computational neuroscience and deep learning. We show that partially trained
deep Spiking Neural Networks (SNNs) implemented on neuromorphic hardware can
rapidly adapt online to new classes of data within a domain. SOEL updates
trigger when an error occurs, enabling faster learning with fewer updates.
Using gesture recognition as a case study, we show SOEL can be used for online
few-shot learning of new classes of pre-recorded gesture data and rapid online
learning of new gestures from data streamed live from a Dynamic Active-pixel
Vision Sensor to an Intel Loihi neuromorphic research processor.
