  Neural architectures in organisms support efficient and robust control that
is beyond the capability of engineered architectures. Unraveling the function
of such architectures is challenging; their components are highly diverse and
heterogeneous in their morphology, physiology, and biochemistry, and often obey
severe speed-accuracy tradeoffs; they also contain many cryptic internal
feedback pathways (IFPs). We claim that IFPs are crucial architectural features
that strategically combine highly diverse components to give rise to optimal
performance. We demonstrate this in a case study, and additionally describe how
sensing and actuation delays in standard control (state feedback, full control,
output feedback) give rise to independent and separable sources of IFPs. Our
case study is an LQR problem with two types of sensors, one fast but sparse and
one dense but slow. Controllers using only one type of sensor perform poorly,
often failing even to stabilize; controllers using both types of sensors
perform extremely well, demonstrating a strong diversity-enabled sweet spot
(DESS). We demonstrate that IFPs are key in enabling this DESS, and
additionally that with IFPs removed, controllers with delayed sensing perform
poorly. The existence of strong DESS and IFP in this simple example suggest
that these are fundamental architectural features in any complex system with
diverse components, such as organisms and cyberphysical systems.
