Descending Predictive Feedback: From Optimal Control to the Sensorimotor System

TL;DR

This paper explains descending predictive feedback in the central nervous system using optimal control theory, showing that sensorimotor constraints naturally lead to DPF patterns similar to those observed in neuroscience.

Contribution

It introduces an optimal control framework to analyze DPF, linking control problems with neural feedback phenomena and proposing the SLS controller as a model for sensorimotor systems.

Findings

Small deviations from ideal control necessitate DPF.

SLS controller exhibits DPF patterns compatible with predictive coding.

Model accommodates neural signaling delays and restrictions.

Abstract

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.