The response of MSTd neurons to perturbations in target motion during ongoing smooth-pursuit eye movements.

Several regions of the brain are involved in smooth-pursuit eye movement (SPEM) control, including the cortical areas MST (medial superior temporal) and FEF (frontal eye field). It has been shown that the eye-movement responses to a brief perturbation of the visual target during ongoing pursuit increases with higher pursuit velocities. To further investigate the underlying neuronal mechanism of this nonlinear dynamic gain control and the contributions of different cortical areas to it, we recorded from MSTd (dorsal division of the MST area) neurons in behaving monkeys (Macaca mulatta) during step-ramp SPEM (5-20 degrees /s) with and without superimposed target perturbation (one cycle, 5 Hz, +/-10 degrees /s).

TMS evidence for smooth pursuit gain control by the frontal eye fields.

Smooth pursuit eye movements are used to continuously track slowly moving visual objects. A peculiar property of the smooth pursuit system is the nonlinear increase in sensitivity to changes in target motion with increasing pursuit velocities. We investigated the role of the frontal eye fields (FEFs) in this dynamic gain control mechanism by application of transcranial magnetic stimulation.

Neural activity in cortical areas MST and FEF in relation to smooth pursuit gain control.

Two cortical areas that crucially contribute to the generation and maintenance of smooth pursuit eye movements (SPEM) are the medial superior temporal area (MST) and the pursuit area of the frontal eye fields (FEF). They both project to the brainstem premotor structures via different parallel pathways. A special property of the pursuit system is the increased sensitivity to retinal image motion for increasing pursuit velocities (dynamic gain control), which might be attributed to the FEF.

A theory of the dual pathways for smooth pursuit based on dynamic gain control.

The smooth pursuit eye movement (SPEM) system is much more sensitive to target motion perturbations during pursuit than during fixation. This sensitivity is commonly attributed to a dynamic gain control mechanism. Neither the neural substrate nor the functional architecture for this gain control has been fully revealed.

Nonlinear and higher-order approaches to the encoding of natural scenes.

Linear operations can only partially exploit the statistical redundancies of natural scenes, and nonlinear operations are ubiquitous in visual cortex. However, neither the detailed function of the nonlinearities nor the higher-order image statistics are yet fully understood. We suggest that these complicated issues can not be tackled by one single approach, but require a range of methods, and the understanding of the crosslinks between the results.