  Neuromorphic computing exploits the dynamical analogy between many physical
systems and neuron biophysics. Superconductor systems, in particular, are
excellent candidates for neuromorphic devices due to their capacity to operate
in great speeds and with low energy dissipation compared to their silicon
counterparts. In this study we revisit a prior work on Josephson Junction-based
"neurons" in order to identify the exact dynamical mechanisms underlying the
system's neuron-like properties and reveal new complex behaviors which are
relevant for neurocomputation and the design of superconducting neuromorphic
devices. Our work lies at the intersection of superconducting physics and
theoretical neuroscience, both viewed under a common framework, that of
nonlinear dynamics theory.
