A Stochastic Optimal Control Model with Internal Feedback and Velocity Tracking for Saccadic Eye Movements. (July 2021)

• Record Type: Journal Article
• Authors: Varsha, V.; Murthy, Aditya; Padhi, Radhakant
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Stochastic optimal control -- Internal feedback -- Velocity tracking -- Saccadic eye movement; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102679
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A tracking optimal control formulation with internal feedback has been developed. A velocity-tracking controller for saccadic eye movement generation in the presence of noise is proposed using the new formulation. The model explains saccadic behavior across amplitudes and directions. This study provides another example suggesting that redundancy is an important principle of the motor system. Abstract: A new stochastic optimal control model with internal feedback and velocity tracking is presented in this paper to study saccadic eye movement control. Recent evidence from neurophysiological studies of superior colliculus suggests the presence of a dynamic input to the saccade generation system that encodes saccade velocity, rather than just the static information of the desired saccade amplitude and direction. The new evidence makes it imperative to test if the saccadic control system can use a desired velocity input to achieve accurate behavioral outcomes. To test such a velocity-based architecture of saccade control, a new optimal control tracking model that incorporated two unique characteristics of internal feedback and stochasticity has been developed. The proposed model was validated using behavioral data of saccades generated by healthy human subjects in an experimental setup. The model was able to generate displacement and velocity trajectories of horizontal saccades made to different amplitudes and predict saccades made to vertical and oblique directions. It captured the main sequence relationship between saccade amplitude and peak velocities that were observed in the behavioral data. This paper presents a novel optimal control framework with tracking and internal feedback and proposes the first-ever model of the saccadic system that uses an alternate interpretation of velocity-based control, contrary to the dominant end-point based models available in the literature.