Abstract

The primate brain intelligently processes visual information from the world as the eyes move constantly. The brain must take into account visual motion induced by eye movements, so that visual information about the outside world can be recovered. Certain neurons in the dorsal part of monkey medial superior temporal area (MSTd) play an important role in integrating information about eye movements and visual motion. When a monkey tracks a moving target with its eyes, these neurons respond to visual motion as well as to smooth pursuit eye movements. Furthermore, the responses of some MSTd neurons to the motion of objects in the world are very similar during pursuit and during fixation, even though the visual information on the retina is altered by the pursuit eye movement. We call these neurons compensatory pursuit neurons. In this study we develop a computational model of MSTd compensatory pursuit neurons based on physiological data from single unit studies. Our model MSTd neurons can simulate the velocity tuning of monkey MSTd neurons. The model MSTd neurons also show the pursuit compensation property. We find that pursuit compensation can be achieved by divisive interaction between signals coding eye movements and signals coding visual motion. The model generates two implications that can be tested in future experiments: (1) compensatory pursuit neurons in MSTd should have the same direction preference for pursuit and retinal visual motion; (2) there should be non-compensatory pursuit neurons that show opposite preferred directions of pursuit and retinal visual motion.

Highlights

  • When we pursue a moving target with our eyes on a static background, the target is relatively static in the foveal region of our visual field

  • We find that pursuit compensation can be achieved by divisive interaction between the eye-movement and visual motion signals combined with shunting computations

  • (4) We suggest that, on a single cell level, pursuit compensation responses to different visual motion and pursuit directions can be explained by a compensation mechanism that operates on the vector component of visual motion along the neuron’s preferred pursuit direction

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Summary

Methods

Our model can account for two main properties of pursuit compensatory neurons in MSTd: a) the sigmoid shape of the velocity tuning curve, and b) the pursuit compensation response to visual motion. The model provides implications that can be tested in physiological experiments (see discussion). We will demonstrate the computations of a sample model MSTd neuron. The output of this neuron shows both velocity tuning characteristic of MSTd neurons in vivo and the pursuit compensatory property

Results
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Conclusion
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