Padhi, R. | Search bioRxiv

bioRxiv2020

A velocity plan with internal feedback control best explains modulation of saccade kinematics during eye-hand coordination.

Fast movements like saccadic eye movements that occur in the absence of sensory feedback are often thought to be under internal feedback control. In this framework, a desired input in the form of desired displacement signal is widely believed to be encoded in a spatial map of the superior colliculus (SC). This is then converted into a dynamic velocity signal that drives the oculomotor neurons. However, recent evidence has shown the presence of a dynamic signal within SC neurons, which correlates with saccade velocity. Hence, we used models based on optimal control theory to test whether saccadic execution could be achieved by a velocity based internal feedback controller. We compared the ability of a trajectory control model based on velocity to that of an endpoint control model based on final displacement to capture saccade behavior of modulation of peak saccade velocity by the hand movement, independent of the saccade amplitude. The trajectory control model tracking the desired velocity in optimal feedback control framework predicted this saccade velocity modulation better than an endpoint control model. These results suggest that the saccadic system has the flexibility to incorporate a velocity plan based internal feedback control that is imposed by task context. NEW & NOTEWORTHYWe show that the saccade generation system may use an explicit velocity tracking controller when demand arises. Modulation of peak saccade velocity due to modulation of the velocity of the accompanying hand movement was better captured using a velocity tracking stochastic optimal control model compared to an endpoint model of saccade control. This is the first evidence of trajectory planning and control for the saccadic system based on optimal control theory.

neuroscience↗

bioRxiv2020

A displacement and velocity based dual model of saccadic eye movements best explains kinematic variability

Noise is a ubiquitous component of motor systems which leads to behavioral variability of all types of movements, including saccadic eye movements. Nonetheless, systems-based models of saccadic eye movements are deterministic and do not explain the observed saccade variability, only their central tendencies. Using stochastic models, we studied the variability in saccade behavior to test and distinguish between previously proposed deterministic saccade models. For this, the inter-trial variability in saccade displacement trajectories of human subjects was quantified while they performed repeated saccadic eye movements to a peripheral target. Based on fits to the data, we showed that existing models based on either displacement or velocity failed to capture the observed patterns in the variability of saccade trajectories. However, the observed behavior was captured by a dual control system, using a combination of displacement and velocity signal. The proposed model fits the mean displacement trajectory as well as the existing deterministic models. Taken together, our results suggest that the saccade system uses both desired displacement and velocity information. New and NoteworthyWe studied saccade behavior with a focus on the variability of the saccade trajectory. A stochastic model of the saccade system suggests that a dual control involving the control of displacement and velocity explains saccade behavior better than previously proposed models that utilize only displacement or velocity information. Our study resolves previous ambiguity regarding the use of displacement or velocity signals to guide saccades and provides a natural explanation for neural recordings that indicate multiplexing of displacement and velocity related information in the firing activity of neurons in the superior colliculus, a critical node in the oculomotor network that codes for saccadic eye movements.

neuroscience↗