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• W1988609198 abstract "Recent experiments have revealed an area of visual cortex that provides a velocity error signal which enables the eye to learn to pursue targets when they move in a predictable way. Recent experiments have revealed an area of visual cortex that provides a velocity error signal which enables the eye to learn to pursue targets when they move in a predictable way. Eye movements exist to make up for our visual defects. The most debilitating is that our retinal receptors are very slow, so that we cannot see properly when the retinal image is moving. Usually, this is because of movement of the head, and the resulting slippage of the entire visual scene generates a powerful reflex, the optokinetic response, which moves the eye in such a way as to reduce the retinal slip: a simple negative feedback system, in which retinal slip velocity is in effect an error signal. The neural circuits for this response are relatively simple, located for the most part in the brainstem. Here, neurons coding for large-scale retinal slip velocity in different directions send this information to neurons in the vestibular nuclei whose function — with help from the semicircular canals — is to estimate head velocity, and thus in turn to generate equal and opposite compensatory eye movements [1Carpenter R.H.S. Movements of the Eyes.2nd edn. Pion, London1988Google Scholar]. There is, however, another way that retinal slip can arise, which poses more of a computational problem. A cat intent on a mouse running through undergrowth needs the retinal image of its prey to be stationary, but if it achieves this there will be a powerful signal from the optokinetic mechanism generated by the backwards retinal slip of the undergrowth itself, which will tend to hold the eye firmly stationary. So what is needed is a system that can selectively inhibit optokinesis except for a defined target region, and one that can also continue the eye’s tracking even when the mouse is briefly obscured by leaves and branches [2Collewijn H. Tamminga E.P. Human smooth pursuit and saccadic eye movements during voluntary pursuit of different target motions on different backgrounds.J. Physiol. 1984; 351: 217-250Crossref PubMed Scopus (277) Google Scholar, 3Becker W. Fuchs A.F. Prediction in the oculomotor system: smooth pursuit during transient disappearance of a visual target.Exp. Brain Res. 1985; 57: 562-575Crossref PubMed Scopus (292) Google Scholar]. This in turn implies prediction of the mouse’s path and velocity, much as an anti-aircraft gun predicts the future position of a plane. But the task is made hugely more difficult by the fact mentioned earlier, that retinal information is so very slow: it takes some 40milliseconds or so to reach even the lowest levels of the brain, and simple visual reaction times are of the order of 180milliseconds or more. All of this demands a control system of some sophistication, which is both flexibly selective and capable of learning and therefore of prediction. As might be expected, the oculomotor sub-system that generates these kinds of movements — the smooth pursuit system — has evolved only relatively recently, mostly in predator species whose retina contains a fovea, a central region specialised for high-definition vision [4Walls G.L. The evolutionary history of eye movements.Vision Res. 1962; 2: 69-80Crossref Scopus (154) Google Scholar, 5Pola J. Wyatt H.J. Smooth movement: response characteristics, stimuli and mechanisms.in: Carpenter R.H.S. Eye Movements. MacMillan, London1992: 138-156Google Scholar, 6Missal M. Lefèvre P. Crommelinck M. Roucoux A. Evidence for high-velocity smooth pursuit in the trained cat.Exp. Brain Res. 1995; 106: 509-512Crossref PubMed Scopus (12) Google Scholar]. The learning and prediction are easy to demonstrate (Figure 1) [7Travis R.C. Dodge R. Ocular pursuit of objects which temporarily disappear.J. Exp. Psychol. 1930; 13: 98-112Crossref Scopus (3) Google Scholar]. Asked to follow a target moving repetitively and predictably — a pendulum, for example — at first one’s eye movements are relatively poorly matched to target velocity; but within a very few repetitions of the pattern of movement there is a dramatic improvement in performance. We can show that this is because the system is actively predicting the motion by suddenly interposing a mask that covers part of the target’s movement: despite the lack of visual input the eye continues to track the invisible movement, albeit not quite as accurately (below, Figure 1). So it is clear that smooth pursuit is not merely driven directly by the error signal: it must contain some kind of predictive model. This in turn implies that errors must be able to tweak the model in addition to driving the response in the first place – the system has to use regulatory parametric feedback as well as the much more familiar direct negative feedback. A recent paper by Megan Carey and colleagues [8Carey M.R. Medina J.F. Lisberger S.G. Instructive signals for motor learning from visual cortical area MT.Nat. Neurosci. 2005; 8: 813-816Crossref PubMed Scopus (37) Google Scholar], working in Steve Lisberger’s lab, has provided some welcome insight into the neural mechanisms that may underlie these processes of instruction and learning, linking them to an area of cortex (medial temporal or MT) where neurons have long been known to carry signals related to retinal slip, of a kind that would make them good candidates for providing error information for regulating smooth pursuit [9Maunsell J.H. van Essen D.C. Functional properties of neurons in middle temporal visual area of the macaque monkey. 1. Selectivity for stimulus direction, speed and orientation.J. Neurophysiol. 1983; 49: 1127-1147Crossref PubMed Scopus (1044) Google Scholar, 10Ferrera V.P. Lisberger S.G. Neuronal responses in visual areas MT and MST during smooth pursuit target selection.J. Neurophysiol. 1997; 78: 1433-1446Crossref PubMed Scopus (81) Google Scholar]. Macaques were trained to follow a repetitive visual target moving horizontally at a constant velocity [8Carey M.R. Medina J.F. Lisberger S.G. Instructive signals for motor learning from visual cortical area MT.Nat. Neurosci. 2005; 8: 813-816Crossref PubMed Scopus (37) Google Scholar]. This task provides a good background on which to study how the smooth pursuit system learns to predict target movement. A previous study from the lab [11Medina J.F. Carey M.R. Lisberger S.G. The representation of time for motor learning.Neuron. 2005; 45: 157-167Abstract Full Text Full Text PDF PubMed Scopus (64) Google Scholar] had shown that, if a vertical perturbation is introduced at a particular point on each sweep, the pursuit system soon learns to anticipate it. The learning is revealed by occasionally presenting a sweep in which the perturbation does not occur: in these ‘probe’ trials, the eyes nevertheless persist in making the expected vertical deviation, despite the absence of an error. In this more recent paper, instead of a real error signal, a vertical perturbation was induced by microstimulation of an appropriate part of MT. The time-course of the resultant vertical velocity of the eye is now more complex, because the evoked upward eye movement evokes actual retinal slip that feeds back into the system to generate a corrective downward response to counteract it. In probe trials, with no stimulation of MT, a learned component of the response is revealed (Figure 2), just as in the previous study. This in itself is not particularly informative about the role of MT in smooth pursuit learning. Stimulation almost anywhere in the oculomotor system would be expected to do something of the sort, because of the error signal that is bound to arise through visual feedback whenever the eye is artificially perturbed. To control for this, a second experiment was performed: this time, visual feedback was prevented by stabilising the target, moving it vertically by an amount exactly equal to the vertical eye movement at every moment (Figure 3). In this way, vertical retinal slip is eliminated, and as a result of this lack of feedback, the perturbation is more sustained; and once again, in probe trials the eye moves vertically even though MT has not been stimulated. Another difference is that because of the absence of corrective feedback, the response is no longer complicated by a second, compensatory phase in opposition to the first. This elegant and convincing experiment therefore suggests strongly that MT is part of a route by which tracking errors both initiate immediate correction, and also cause information to be stored that results in anticipation of the perturbation in future. What it does not tell us, unfortunately, is just how the learning itself is implemented. This has been an area of active speculation for several decades [12Bahill A.T. McDonald J.D. Model emulates human smooth pursuit system producing zero-latency target tracking.Biol. Cybern. 1983; 48: 213-222Crossref PubMed Scopus (67) Google Scholar, 13Robinson D.A. Gordon J.L. Gordon S.E. A model of the smooth pursuit eye movement system.Biol. Cybern. 1986; 55: 43-57Crossref PubMed Scopus (388) Google Scholar, 14Krauzlis R.J. Lisberger S.G. A control systems model of smooth pursuit eye movements with realistic emergent properties.Neural Comput. 1989; 1: 116-122Crossref Google Scholar, 15Barnes G.R. Asselman P.T. The mechanism of prediction in human smooth pursuit eye movements.J. Physiol. 1991; 439: 439-461Crossref PubMed Scopus (211) Google Scholar, 16Barnes G. Grealy M. Collins S. Volitional control of anticipatory ocular smooth pursuit after viewing, but not pursuing, a moving target: evidence for a reafferent velocity store.Exp. Brain Res. 1997; 116: 445-455Crossref PubMed Scopus (56) Google Scholar, 17Churchland M.M. Lisberger S.G. Experimental and computational analysis of monkey smooth-pursuit eye movements.J. Neurophysiol. 2001; 86: 741-759Crossref PubMed Scopus (40) Google Scholar], but as yet we are not much nearer understanding even the type of learning process that is going on, let alone the neuronal details of its implementation. But now that we have a way of injecting instructional signals into the system, without visual feedback coming up from behind and complicating things, there is perhaps some hope of making progress." @default.
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• W1988609198 title "Visual Pursuit: An Instructive Area of Cortex" @default.
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