This paper gives an overview of recent progress on 1) online learning algorithms with spiking neurons 2) neuromorphic platforms that efficiently run these algorithms with a focus on implementation using analog-non-volatile memory (aNVM) as electronic synapses. Design considerations and challenges for using aNVM synapses such as requirements for device variability, multilevel states, programming energy, array-level connectivity, wire energy, fan-in/fan-out, and IR drop are presented. Future research directions and integration challenges are summarized. Algorithms based on spiking neural networks are promising for energy efficient real-time learning, but cycle-to-cycle device variations can significantly impact learning performance. Our analysis suggests that wires are increasingly important for energy considerations, especially for large systems.