2021/03/31 by Jing Shuang Li, Li, Jing Shuang, Anish A. Sarma +3
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Mathematics · Neuroscience · #FOS: Biological sciences #FOS: Electrical engineering #FOS: Mathematics #Motor Control and Adaptation #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #Optimization and Control (math.OC) #Systems and Control (eess.SY) #Visual perception and processing mechanisms #cs.SY #eess.SY #electronic engineering #information engineering #math.OC #q-bio.NC
paper · pdf · doi:10.48550/arxiv.2103.16812
arxiv created 2021/03/31 · openalex publication_date 2021/03/31 · arxiv updated 2021/04/01 · openalex created_date 2021/04/13 · openalex updated_date 2026/07/28
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.