In the 2016 movie Passengers, the crew of a spacecraft bound for a distant planet had access to a scanning chamber known as Autodoc that could instantly diagnose their medical problems and even predict the time of their death. I'm reminded of this, and countless other sci-fi plots, as I strip off my robe and step semi-naked into the gleaming capsule of the Neko Body Scan. Like Autodoc, it promises to conduct a comprehensive examination of my health – inside and out – within minutes, and, while unable to estimate the timing of my demise (yet), it can identify whether I'm at imminent or future risk of developing some of the biggest killers and causes of chronic ill health. Healthy as I may feel on the outside, the prospect of learning whether there is some hidden nastiness lurking on my health horizon, feels too tempting to refuse. The doors of the pod slide shut, and a soothing female voice instructs me to close my eyes and keep still.

The field of neuromorphic computing is in a period of active exploration. While many tools have been developed to simulate neuronal dynamics or convert deep networks to spiking models, general software libraries for learning rules remain underexplored. This is partly due to the diverse, challenging nature of efforts to design new learning rules, which range from encoding methods to gradient approximations, from population approaches that mimic the Bayesian brain to constrained learning algorithms deployed on memristor crossbars. To address this gap, we present Neko, a modular, extensible library with a focus on aiding the design of new learning algorithms. We demonstrate the utility of Neko in three exemplar cases: online local learning, probabilistic learning, and analog on-device learning. Our results show that Neko can replicate the state-of-the-art algorithms and, in one case, lead to significant outperformance in accuracy and speed. Further, it offers tools including gradient comparison that can help develop new algorithmic variants. Neko is an open source Python library that supports PyTorch and TensorFlow backends.