  As a means of dynamically reconfiguring the synaptic weight of a
superconducting optoelectronic loop neuron, a superconducting flux storage loop
is inductively coupled to the synaptic current bias of the neuron. A standard
flux memory cell is used to achieve a binary synapse, and loops capable of
storing many flux quanta are used to enact multi-stable synapses. Circuits are
designed to implement supervised learning wherein current pulses add or remove
flux from the loop to strengthen or weaken the synaptic weight. Designs are
presented for circuits with hundreds of intermediate synaptic weights between
minimum and maximum strengths. Circuits for implementing unsupervised learning
are modeled using two photons to strengthen and two photons to weaken the
synaptic weight via Hebbian and anti-Hebbian learning rules, and techniques are
proposed to control the learning rate. Implementation of short-term plasticity,
homeostatic plasticity, and metaplasticity in loop neurons is discussed.
