Our Research Approach
Our goal is to discover general principles of the organization and operation of neural systems. Our approach is to study the eye movements of rhesus monkeys so that we can take advantage of the ease of controlling, measuring, and quantifying eye movement behavior while we monitor the activity of one or more neurons in the circuit that generates the movements. We study mainly smooth pursuit eye movements because they comprise a motor system with the same cortical and sub-cortical architecture as other sensory-motor systems. Thus, we can learn how the brain transforms sensory inputs into coordinated movements, through analysis of how it moves the eyes.
The basic circuit for pursuit is known. Sensory inputs arise from the middle temporal visual area and the “motor cortex” is the smooth eye movement region of the frontal eye fields (FEFsem). Other well studied areas include the floccular complex of the cerebellum and the final brainstem motor circuits. Opportunities exist for to greatly advance our knowledge of the system through analysis of the basal ganglia, and the pontine nuclei that relay signals from the cerebral cortex to the cerebellum.
We investigate multiple operations that are performed by the pursuit circuit to transform visual motion signals into commands for eye velocity. These include: sensory decoding to convert the population response in area MT into estimates for target speed and direction; gain control that modulates the strength of visual-motor transmission; and neural integration that creates a sustained eye velocity command for steady-state pursuit AND transforms a velocity command into the position signals needed to control the force in the eye muscles. We think that these simple building blocks work together to create complex features of pursuit such as Bayesian inference, the effects of reward size, and the responses when multiple moving targets are present.
Our research on motor learning in pursuit eye movements started with the observation that learning induces appropriate changes in Purkinje cell simple-spike output from the floccular complex to drive the learned behavior. We also found that the climbing-fiber input responds to learning instructions in a way that is appropriate to teach the simple-spike output, and we showed that learning occurs both in eye movements and in simple-spike firing after a single climbing-fiber input. But, we also recognize that learning is an emergent property of the operation of a large circuit, and our current research aims to elucidate the sites and mechanisms of neural learning at multiple sites in the complete circuit.
We rely on exquisite control over a very precise behavior, quantitative and accurate measurements of eye movements, and recordings from single neurons throughout the pursuit circuit. We analyze our data quantitatively, frequently using methods that are derived from theoretical approaches, and we use computer simulations and computational modeling to assemble our data into an understanding of how a full circuit works.