Neuromorphic engineering, while originally focused on brain-inspired analog circuits, has now evolved to cover non von Neumann computer architectures and spiking neural network (SNN) algorithms. The major advantage of using neuromorphic systems is expected to be low-energy implementation of machine learning and pattern recognition algorithms. The savings arise from event-driven operation or using physics of analog circuits for computing or a combination of the two. Such low-energy machine learners are essential for the increasingly popular edge-computing paradigm. An intriguing applications of such edge-computing systems is in wearable and implantable devices for biomedical systems. Such devices operate from an extremely low energy budget due to problems in frequent replacement of batteries—hence, neuromorphic low-power circuits are relevant. Moreover, due to the continuous time operation of SNN, they may be more suited to handle continuous time biomedical signals like ECG, EMG or EEG than their artificial neural network (ANN) counterparts. In this talk, I will first review the recent progress in the area of neuromorphic circuits and algorithms for deep neural networks. Then I will provide concrete examples of using such circuits and algorithms in two systems: (i) implantable brain-machine interfaces for intention decoding and (ii) wearable devices to decode gestures using EMG. I will conclude the talk with some directions of future research.