Online learning in SNNs with e-prop and Neuromorphic Hardware

Online learning in neural networks has the potential to transform AI research. By enabling new information to be assimilated into existing systems, platforms can be adaptive to unseen data and can personalise performance to an individual. A common approach in providing AI to a user is to send queries to a remote cloud service which processes the information and sends back a response. Neuromorphic hardware offers an alternate solution by providing a dedicated computing platform from which neural networks can be run locally and efficiently. This work explores the potential of the SpiNNaker neuromorphic hardware to run the eligibility propagation (e-prop) algorithm on chip whilst learning online in real time.