  Spiking Neural Networks (SNNs) have recently gained popularity as machine
learning models for on-device edge intelligence for applications such as mobile
healthcare management and natural language processing due to their low power
profile. In such highly personalized use cases, it is important for the model
to be able to adapt to the unique features of an individual with only a minimal
amount of training data. Meta-learning has been proposed as a way to train
models that are geared towards quick adaptation to new tasks. The few existing
meta-learning solutions for SNNs operate offline and require some form of
backpropagation that is incompatible with the current neuromorphic
edge-devices. In this paper, we propose an online-within-online meta-learning
rule for SNNs termed OWOML-SNN, that enables lifelong learning on a stream of
tasks, and relies on local, backprop-free, nested updates.
