Smooth Pursuit Eye Movements

copy of eye velocity, which then drives pursuit (e.g. [93]). The latter approach has some advantages, especially regarding the problem of the long latency of visual processing, which makes the use of a simple high-gain feedback loop problematic. It is also supported by recent experimental evidence: it has been shown that the cortical middle superior temporal area (MST) contains neurons which code target motion in space (for review, see [87]), and that thalamic neurons carry smooth pursuit signals which are suited to convey an efference copy to the cortex [94].

While figure 2 shows the basic information processing of the two hypotheses, the various boxes in this processing scheme may contain mathematical descriptions of the underlying processing from simple gain elements, delays or linear differential equations, as in [91], to more complex systems of nonlinear differential equations which are used to model neural networks. It is worth to note that all dynamic computational models, whether on a systems level or describing in detail the dynamics of ion channels of single neurons, rely on the same basic building blocks, coupled differential equations.

All pathways for pursuit pass through the cerebellum; therefore, most models have concentrated on the role of the cerebellar pathways, especially those passing through the floccular lobe. The role of the cortical structures (pursuit region of the FEFs and MST), their downstream pathways (dorsolateral pontine nuclei and nucleus reticularis tegmenti pontis), and their specific contribution has received less attention so far. Neurophysiology suggests that the FEF pathway is more related to signals on eye acceleration, i.e. changes in pursuit velocity, while MST is thought to convey signals related to ongoing pursuit [95]. FEF has also been implicated in pursuit gain control [96]: rapid variations in target velocity have a greater effect if pursuit velocity is high. Similarly, pursuit onset is slower than pursuit offset. While earlier models assumed a switch in pursuit pathways [97], one pathway for pursuit onset, and one for offset, a continuous gain control is now discussed [98, 99].

Gain control may also be related to another relevant feature of smooth pursuit, its predictive nature. Despite the visual latency, pursuit tracking of simple motions, such as ramp-like or sinusoidal target movement, can reach unity gain with zero latency. Thus, some form of predictive control must take place. Current models propose memory-based mechanisms [100] or adaptive control implementing a predictive model of target dynamics [101] to explain the experimental findings. It is also not clear whether the predictive aspects of pursuit control are implemented in cortical areas [101], or in the cerebellum [102], or in both.

Since pursuit movements are almost always accompanied by saccadic eye movements, a recent model proposes how switching between both modes can be achieved together with predictive aspects of saccades and pursuit [103].

Combined Eye-Head Movements

Combined eye-head movements occur when the head is passively perturbed and the eyes compensate by the VOR. However, under natural circumstances saccadic gaze shifts and smooth pursuit consist of a combination of eye and head movements, especially when the target eccentricity is too large to be reached with the eye alone. Active combined eye-head movements raise several questions [104], for example whether the VOR is active during the gaze shift, or whether the local feedback loops in the saccadic system operate on gaze (eye plus head) or eye-in-head signals. While it is usually accepted that the VOR is shut off during the gaze shift, models on combined eye-head gaze shifts reached different conclusions concerning the feedback loops: while most models assume that gaze is the controlled variable [105–107], others propose that eye and head movements are controlled separately with the head controller influencing the saccadic burst generator for the eye [108]. The 3-D behavior of eye and head during gaze shifts has successfully been explained by an elegant model [109] which shows how the eye may anticipate the final head position. Finally, a recent neural network model showed how superior colliculus and cerebellum may interact for combined eye-head gaze shifts [50].

Conclusions

Basically for all aspects of eye movement control, computational models do exist. The vast majority of these models are based on a systems level approach or use neural networks with firing rate neurons. While most models concentrate on specific aspects of eye movements, there are some attempts to provide models putting together several of the ocular motor subsystems. To be useful, future models need to pursue such a holistic approach to eye movements, and at the same time try to link the systems level approach to the underlying neural mechanisms.

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Stefan Glasauer Department of Neurology Klinikum Grosshadern Marchioninistrasse 15

DE–81377 Munich (Germany)

Tel. 49 89 7095 4835, Fax 49 89 7095 4801, E-Mail sglasauer@nefo.med.uni-muenchen.de