Dendritic Learning in Superconducting Optoelectronic Networks.

Superconducting Optoelectronic Networks (SOENs) combine photonics and superconductors to instantiate computing systems that approach the fundamental limits of information processing in terms of speed and scalability. Overcoming the engineering challenges of integrating these technologies into one system has consistently been aided by using neuro-inspired architectures. SOENs are natively Spiking Neural Networks (SNNs) of loop neurons , which themselves are comprised of many subsequent dendrites organized into intricate morphologies. Therefore, SOENs at scale may embody highly complex superstructures that demand commensurate learning methods. To that end, we here propose a simple activity-based update rule (the arbor-update ) for dendritic learning that is found to successfully classify nine-pixel images with a single neuron. We test two amendments to the arbor-update on a winner-take-all (WTA) mutually inhibitory three-neuron network. Both elastic weight collision with dynamical boundaries and intermittent validation are found to improve convergence time and conditions. The arbor-update and its variants are scalable with SOENs and may even map to other systems. Importantly, all proposed learning methods are expected to be entirely implementable for on-chip learning in SOENs.