  Building on our recent work on {\em neuromimetic control theory}, new results
on resilience and neuro-inspired quantization are reported. The term
neuromimetic refers to the models having features that are characteristic of
the neurobiology of biological motor control. As in previous work, the focus is
on what we call {\em overcomplete} linear systems that are characterized by
larger numbers of input and output channels than the dimensions of the state.
The specific contributions of the present paper include a proposed {\em
resilient} observer whose operation tolerates output channel intermittency and
even complete dropouts. Tying these ideas together with our previous work on
resilient stability, a resilient separation principle is established. We also
propose a {\em principled quantization} in which control signals are encoded as
simple discrete inputs which act collectively through the many channels of
input that are the hallmarks of the overcomplete models. Aligned with the
neuromimetic paradigm, an {\em emulation} problem is proposed and this in turn
defines an optimal quantization problem. Several possible solutions are
discussed including direct combinatorial optimization, a Hebbian-like iterative
learning algorithm, and a deep Q-learning (DQN) approach. For the problems
being considered, machine learning approaches to optimization provide valuable
insights regarding comparisons between optimal and nearby suboptimal solutions.
These are useful in understanding the kinds of resilience to intermittency and
channel dropouts that were earlier demonstrated for continuous systems.
