  Brain dynamics can exhibit narrow-band nonlinear oscillations and
multistability. For a subset of disorders of consciousness and motor control,
we hypothesize that some symptoms originate from the inability to spontaneously
transition from one attractor to another. Using external perturbations, such as
electrical pulses delivered by deep brain stimulation devices, it may be
possible to induce such transition out of the pathological attractors. However,
the induction of transition may be non-trivial, rendering the current open-loop
stimulation strategies insufficient. In order to develop next-generation neural
stimulators that can intelligently learn to induce attractor transitions, we
require a platform to test the efficacy of such systems. To this end, we
designed an analog circuit as a model for the multistable brain dynamics. The
circuit spontaneously oscillates stably on two periods as an instantiation of a
3-dimensional continuous-time gated recurrent neural network. To discourage
simple perturbation strategies such as constant or random stimulation patterns
from easily inducing transition between the stable limit cycles, we designed a
state-dependent nonlinear circuit interface for external perturbation. We
demonstrate the existence of nontrivial solutions to the transition problem in
our circuit implementation.
