Saccades modulate the relationship between visual motion and smooth eye movement. Before a saccade, pursuit eye movements reflect a vector average of motion across the visual field. After a saccade, pursuit primarily reflects the motion of the target closest to the endpoint of the saccade. We tested the hypothesis that the saccade produces a spatial weighting of motion around the endpoint of the saccade. Using a moving pursuit stimulus that stepped to a new spatial location just before a targeting saccade, we controlled the distance between the endpoint of the saccade and the position of the moving target. We demonstrate that the smooth eye velocity following the targeting saccade weights the presaccadic visual motion inputs by the distance from their location in space to the endpoint of the saccade, defining the extent of a spatiotemporal filter for driving the eyes. The center of the filter is located at the endpoint of the saccade in space, not at the position of the fovea. The filter is stable in the face of a distracter target, is present for saccades to stationary and moving targets, and affects both the speed and direction of the postsaccadic eye movement. The spatial filter can explain the target-selecting gain change in postsaccadic pursuit, and has intriguing parallels to the process by which perceptual decisions about a restricted region of space are enhanced by attention. The effect of the spatial saccade plan on the pursuit response to a given retinal motion describes the dynamics of a coordinate transformation.

For many behaviors we rely on our senses, which inform the brain about the

world around us, such as the spatial location of an object we would like to grasp or the

motion of a target that we intend to follow with our eyes. In order to execute an

appropriate movement, neural responses in sensory areas must be read out and

transformed into signals meaningful to premotor and motor areas so an accurate motor

command can evolve. I used smooth pursuit eye movements to study the sensory-motor

transformation of visual signals about object motion into an appropriate motor command

for accelerating the eyes. I found that fluctuations in single neuron responses in visual

area MT are predictive of deviations in eye speed during pursuit initiation, which

suggests that responses in MT contribute to noise in the motor output. Further, the

relationship between the sensory and motor variability revealed constraints about the

neural mechanisms underlying the transformation of MT signals into a command for eye

speed: if downstream noise is low, a modified vector averaging computation involving

opponent signals between oppositely tuned neurons could explain the relationship

between responses of single neurons in area MT and eye speed at the initiation of pursuit.

In addition to smooth pursuit eye movements, we studied MT responses during

drifts in eye movements of fixation. I found that deviations in sensory responses not only

predict variation in pursuit initiation, but that deviations in eye velocity during fixation

also modulate the neural responses in MT. I showed that firing rates in MT are modulated because tiny changes in eye velocity generate image motion on the retina. These results demonstrate that image motion due to miniature eye movements is large enough to affect the responses of visual neurons beyond the retina or early cortical visual areas.

This thesis has underscored how tightly sensory and motor systems are

interwoven; visual area MT, in particular, forms an important link between the processing

of visual signals and the generation of motor commands for smooth pursuit eye

movements.

For a learned movement to be effective, it must be produced at the appropriate time, but how this is achieved in the brain remains unknown. Smooth pursuit is a simple oculomotor behavior that exhibits temporally specific learning. Upon repeated exposure to a precisely-timed instructive change in target direction, the pursuit system learns to produce an eye movement that peaks around time when the instructive stimulus is expected to appear. The smooth eye movement region of the frontal eye fields (FEFSEM), a motor cortex for smooth pursuit, contains a spatial map of elapsed time since the initiation of pursuit, and is therefore a good candidate for mediating the temporal specificity of pursuit learning. In chapter 1, I describe a series of electrophysiology experiments demonstrating that the FEFSEM, by virtue of its innate representation of time, may serve to incorporate the salient temporal features of the instructive stimulus into the learned eye movement.

Because movements improve and become ingrained with practice, motor learning is inherently a dynamic process. In chapter 2, I explored the neural changes associated with the dynamics of motor learning by comparing how activity in the FEFSEM and a downstream cerebellar locus for pursuit, the floccular complex, emerged and evolved as the animal learned to produce the desired eye movement. My findings suggest that pursuit learning arises from a combination of neural processes with different dynamics of adaptation, and that the FEFSEM and the floccular complex utilize these processes in different ways to support at least partially separate aspects of pursuit learning.

All ethologically-relevant behavior is dynamic, and biological in origin. To understand the neural underpinnings of motor behavior is then to quantify the limitations placed on organisms as they move through and interact with their environment. This thesis stands as an attempt to understand the way the brain processes information to prepare for movement, and how a network of neurons might give rise to that movement. I chose to investigate oculomotor tracking in non-human primates as a model behavior that allowed excellent spatial and temporal control over the stimulus. First, by carefully controlling the motion of a single target, and measuring the eyes' response to particular perturbations, I mapped out effective constraints on the way the brain processes target motion to guide smooth eye movements. The next set of experiments was motivated by the observation that no two tracking movements are alike, even when faced with identical stimuli. I establish the dynamic relationship between fluctuations in single neurons recorded in the frontal eye fields and concomitant behavioral fluctuations. Utilizing measurements of the covariation between simultaneously recorded neurons, I describe the properties of the network of cells which give rise said fluctuations, which is likely dominated by fluctuations in a small subset of the active population at any moment in time. Taken together, the work I've done first establishes that proper oculomotor tracking likely takes the form of a re-allocation of cortical resources, but one that may be dominated by the activity of a small number of cortical neurons.