  Descending predictive feedback (DPF) is an ubiquitous yet unexplained
phenomenon in the central nervous system. Motivated by recent observations on
motor-related signals in the visual system, we approach this problem from a
sensorimotor standpoint and make use of optimal controllers to explain DPF. We
define and analyze DPF in the optimal control context, revisiting several
control problems (state feedback, full control, and output feedback) to explore
conditions that necessitate DPF. We find that even small deviations from the
unconstrained state feedback problem (e.g. incomplete sensing, communication
delay) necessitate DPF in the optimal controller. We also discuss parallels
between controller structure and observations from neuroscience. In particular,
the system level (SLS) controller displays DPF patterns compatible with
predictive coding theory and easily accommodates signaling restrictions (e.g.
delay) typical to neurons, making it a candidate for use in sensorimotor
modeling.
