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Descending Predictive Feedback: From Optimal Control to the Sensorimotor System

Li, Jing Shuang and Sarma, Anish A. and Doyle, John C. (2021) Descending Predictive Feedback: From Optimal Control to the Sensorimotor System. . (Unpublished)

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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.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Li, Jing Shuang0000-0003-4931-8709
Doyle, John C.0000-0002-1828-2486
Record Number:CaltechAUTHORS:20210510-141350951
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109056
Deposited By: George Porter
Deposited On:10 May 2021 21:47
Last Modified:10 May 2021 21:47

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