Visual Guidance of Smooth-Pursuit Eye Movements: Sensation, Action, and What Happens in Between

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Stephen G. Lisberger1,* 1Howard Hughes Medical Institute, Department of Physiology, and W.M. Keck Foundation Center for Integrative Neuroscience, University of California, San Francisco, San Francisco, CA 94143-0444, USA *Correspondence: [email protected] DOI 10.1016/j.neuron.2010.03.027

Smooth-pursuit eye movements transform 100 ms of visual motion into a rapid initiation of smooth eye movement followed by sustained accurate tracking. Both the mean and variation of the visually driven pursuit response can be accounted for by the combination of the mean tuning curves and the correlated noise within the sensory representation of visual motion in extrastriate visual area MT. Sensory-motor and motor circuits have both housekeeping and modulatory functions, implemented in the cerebellum and the smooth eye movement region of the frontal eye ﬁelds. The representation of pursuit is quite different in these two regions of the brain, but both regions seem to control pursuit directly with little or no noise added downstream. Finally, pursuit exhibits a number of voluntary characteristics that happen on short timescales. These features make pursuit an excellent exemplar for understanding the general properties of sensory-motor processing in the brain.

Introduction the brainstem are well understood. The cortical sensory-motor Something happens. We act. Or not. That simple sequence char- circuits for both saccadic and smooth-pursuit eye movements acterizes most of our lives: Roger Federer returning service, follow the same general outline given above for reaching (Lynch bringing a cup of coffee to our lips, or moving our eyes to look and Tian, 2006). Studies of voluntary eye movements have at an object of interest all follow the same sequence from sensa- elucidated (1) how the parameters of a sensory stimulus are tion to action. Of course, what happens between sensation and estimated from the response of a population of sensory action is what makes us human. Sensory inputs provide a context neurons; (2) sensory and motor sources of motor variation; (3) for thoughts, decisions, and memories, and the consequent the differences between cerebral and cerebellar control of choices between action and inaction. To understand ourselves movement; (4) the processing of signal and noise in sensory- and provide a framework for applying basic research to improve motor circuits; (5) the neural basis for target choice and motor human lives, we need to explicate fully how we process sensory attention; and (6) mechanisms and roles for efference copy inputs, make plans, and act. signals. Thus, eye movements provide an excellent model For somatic movements such as reaching, we know the basic system for obtaining answers to general neuroscience ques- anatomical substrate for using sensory inputs to plan and tions. In addition, eye movements are of particular importance execute movements. Signals from cortical sensory areas are to primates like us, who rely heavily on vision and must move relayed to the motor cortex through specific sensory-motor their eyes to point the foveae at stationary and moving objects areas in the parietal cortex (Rizzolatti et al., 1998). There are of interest. precisely defined recurrent loops from the motor parts of the In the present review, I have two purposes. First, I summarize cerebral cortex to the basal ganglia or the cerebellum, and the work of my own laboratory on smooth-pursuit eye move- back (Middleton and Strick, 2000). Descending pathways from ments over the past 20 years, placing an emphasis on how the the cortex and the brainstem access motor synergies and modu- full body of knowledge we have created fits together into late reflex pathways in the spinal cord, leading to coordinated a conceptual story of how a sensory-motor system works. activity in motoneurons (e.g., Drew et al., 2004). The resulting I attempt to identify areas of possible controversy, disagree- movements are, in general, both accurate and quite precise ment, or alternate hypotheses and to place our work in the (e.g., Shadmehr and Mussa-Ivaldi, 1994). But, we do not under- context of others’. Still, to keep the review accessible to a general stand what happens in the circuits between sensation and readership, many important but specialized points are finessed. action. We know how sensory and motor events are represented Second, I place our knowledge of the neural mechanisms of in the brain, but not how the former are transformed to achieve pursuit eye movements in the larger context of general neurosci- the latter. ence issues in sensation, sensory-motor transformations, and Eye movements, because they have many simplifying prop- motor control. I think this latter purpose is especially important erties as neural systems, offer the opportunity to understand because of the potential value of smooth-pursuit eye move- what happens between sensation and action. For example, ments for obtaining quantitative answers to central questions the organization of the motor effectors is relatively simple for in systems neuroscience, such as the six issues enumerated in eye movements, and the final motor and premotor circuits in the previous paragraph.

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Sensory Drive for Smooth-Pursuit Eye Movements Eye In the 1970s, neuroscientists discovered a specialized set of cortical visual areas that process visual motion (Dubner and Position 4 deg Zeki, 1971; Allman et al., 1973). The middle temporal visual Target area, known as V5 or MT, is at the heart of those areas. It contains neurons that respond selectively to moving targets and that are tuned for the direction and speed of motion across their receptive fields (Maunsell and Van Essen, 1983). Earlier, Rashbass (1961) had shown that smooth-pursuit eye movements, like the neurons in MT, respond selectively to visual Velocity motion. Rashbass used the ‘‘step-ramp’’ target motion shown 10 deg/s at the top of Figure 1. Here, a subject starts by fixating a stationary target. At an unexpected time, the fixation target -200 0 200 400 disappears, and a tracking target appears eccentric in the recep- Time from target motion onset (ms) tive field, moving back toward its initial position. The step-ramp motion creates a competition between target position and Figure 1. Pursuit of Step-Ramp Target Motion motion: if pursuit is driven by signals that report target position From top to bottom, the traces are superimposed eye and target position and relative to the eye, then smooth eye movement should be initi- superimposed eye and target velocity. Dashed and continuous traces show ated toward the position of the target; if pursuit is driven by target target and eye motion. The diagonal arrow points out a saccadic change in eye position, causing the rapid and large downward deflection in the eye motion, then smooth eye movement should take the eye in the velocity trace. The shaded area on the velocity traces indicates target motion direction of target motion, away from target position. As illus- with respect to the eye, namely the image motion that provides the stimulus for trated in Figure 1, reality corresponds to the latter situation. the visual motion system that drives pursuit eye movement. Data are a modern example of the original experiments by Rashbass (1961). Pursuit is initiated in the direction of target motion and a subse- quent saccadic eye movement (arrow in Figure 1) corrects the residual difference between the positions of the eye and target. Indeed, using a computer to stabilize the target with respect to the The convergent findings of a movement and a cortical area that moving eye during steady-state pursuit reveals that steady-state are both selectively responsive to target motion led to tests of the eye velocity persists almost perfectly in the absence of image hypothesis that area MT provides the sensory inputs that drive motion(MorrisandLisberger, 1987).In the absence of acontinuing pursuit. Indeed, lesions within MT caused deficits in the use of drive, the elasticity and viscosity of the orbit would cause the eye visual motion for guiding pursuit eye movements (Newsome to come to a halt within 200 ms. Extraretinal signals must provide et al., 1985). Importantly, MT has a topographic organization so the neural drive to keep the eye moving. Consequently, we think of that different parts of the visual field are represented in an orderly two separate phases of pursuit—initiation versus steady state— way across the full extent of MT. Lesions confined to the parts of with different, retinal versus extraretinal, control signals and prob- MT that process motion signals from defined, small areas of the ably different control strategies in the brain. visual field revealed a motion scotoma for the initiation of pursuit. If the targets used to initiate pursuit moved across unaffected Estimating Sensory Parameters from the Responses parts of the visual field, then the initiation of pursuit was normal, of a Population of Tuned Neurons indicating that the ablations had not affected the animal’s ability The representation of image motion in MT is a ‘‘place code.’’ MT to generate the motor act itself. If the targets moved across the neurons are tuned for the speed and direction of image motion, affected part of the visual field, then monkeys were not able to with different neurons showing maximum responses for different initiate pursuit. Instead, they waited for the target to move outside ‘‘preferred’’ speeds and directions (Maunsell and Van Essen, the region represented by the lesion, or used a saccade to move 1983). Thus, any given target motion causes activity in many the target into an intact region of the visual field. Further experi- MT neurons, with the largest response in the neurons whose ments showed that microstimulation within MT could alter the preferred direction and speed match the target motion. Because speed and direction of pursuit with latencies as short as 25 ms of the tuned responses of MT neurons, neither the firing rate of (Groh et al., 1997; Carey et al., 2005). Thus, it seems reasonable any single neuron nor the average response amplitude across to think of MT as a major source of the sensory inputs that guide neurons provides an unambiguous estimate of target direction pursuit eye movements, with the understanding that other and speed. These estimates must come from comparing the cortical areas also may contribute. responses of neurons across the population. The nature of the Inspection of the velocity traces in Figure 1 reveals that there are place code in MT contrasts with the rate code that represents two very different phases of pursuit. During the latency before the and drives pursuit in the motor system (Groh, 2001). In the initiation of pursuit and the initial rising phase, the target moves floccular complex of the cerebellum, for example, pursuit is rep- with respect to the eye, and there is substantial image motion resented largely by two groups of Purkinje cells that prefer eye across the retina (shaded area in velocity traces) to drive the visual motion either toward the side on which they reside, or downward motion system and pursuit (Lisberger and Westbrook, 1985). (Krauzlis and Lisberger, 1996). Each Purkinje cell participates Later, eye velocity matches target velocity almost perfectly so in all pursuit movements in or near its preferred direction, show- that there is little or no image motion to drive pursuit (ignoring ing firing rates that scale monotonically with pursuit speed the large downward spike of eye velocity caused by the saccade). (Lisberger and Fuchs, 1978).

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2 4 6 8 10 12 14 Preferred speeds Figure 2. Simple Model Population Response in MT Model MT units The circles at the top represent seven model MT Weighted Vector neurons that are tuned for target speed, and the Sum sum average number above each circle indicates the neuron’s preferred speed. From top to bottom, the three 6 deg/s 18 108 108/18=6 lines of neural responses indicate the spikes in 10 deg/s 18 180 180/18=10 model neurons for motion of a bright target at 6/s, motion of a bright target at 10/s, and motion 10 deg/s, dim 9 90 90/9=10 of a dim target at 10 /s. Numbers at the right indicate the estimates of target speed obtained by three different decoding computations.

Figure 2 illustrates the problem of estimating sensory param- of vector averaging to determine the center-of-mass of the pop- eters from a population response. It diagrams a cartoon popula- ulation response is approximately an optimal linear estimator for tion response of seven MT neurons, each tuned for a different a well-behaved population response. We regard vector aver- speed of image motion. Suppose that we deliver the motion of aging as a way of quantifying the content of the population a bright target at a speed of 6/s or 10/s. Because of their speed response in MT, but without any strong implications for an imple- tuning, each neuron responds differently to the two speeds of mentation in the brain. Weighting of each neuron’s response by motion. For the slower speed, the neuron with a preferred speed its preferred speed in the numerator could be accomplished by of 6/s emits six spikes, while the other neurons emit fewer synaptic strengths. Normalization by the total population activity spikes. The population response shows a peak at 6/s. For the in the denominator is somewhat more challenging to implement, faster speed, the neuron with a preferred speed of 6/s now because of the need to divide with neurons, although a number emits only three spikes, while the neuron with a preferred speed of neural mechanisms have been proposed in theoretical and of 10/s now emits six spikes. For each target motion, it is computational papers (Heeger, 1993; Chance et al., 2002). obvious by inspection that we can guess (‘‘estimate’’) the speed The strong relationship between target speed and the magni- of the stimulus by reporting the preferred speed of the neurons tude of the change in eye velocity in the first 100 ms of the with the largest response. To understand how the brain decodes initiation of pursuit (Lisberger and Westbrook, 1985) would be the population response, however, we need a way to perform expected if vector averaging describes the population decoding this ‘‘inspection’’ mathematically. computation used by the brain. Stronger support comes from Simply adding up the total activity across the population the effects of degrading the population response with sampled response yields 18 spikes for the bright targets moving at either motion. When we presented targets that flashed sequentially at 6/s or 10/s and does not allow us to discriminate between the different locations separated systematically in space (Dx) and two speeds. Labeling each neuron according to its preferred time (Dt), we found an illusion of increased estimates of target speed helps. Now, the weighted sum, calculated by summing speed by both pursuit and perception (Churchland and Lis- the product of preferred speed and number of spikes across berger, 2000, 2001). For example, Figure 3C shows time aver- the population, allows us to discriminate between the motion ages of eye velocity in response to target motion at 15/s for of the bright targets at 6/s and 10/s Consider, however, the smooth target motion (black) versus target motion that is flashed smaller responses (Sclar et al., 1990) of the same population of at spatially separated locations every 32 ms (red trace). After model MT neurons for a dim target that moves at 10/s. The a slight delay, the eye velocity for degraded motion rose more weighted sum is smaller for motion at 10/s for the dim than for rapidly to a higher level of eye velocity compared to the eye the bright target: decoding with a weighted sum of the popula- velocity for smooth motion. tion activity risks confounding slow motion of a bright target We think of the magnitude of the initial pursuit response as with fast motion of a dimmer target. a probe for the properties of the visual signals that are driving Adding one more element in the decoding computation solves pursuit. This view is justified by the fact that the latency from the problem. An unambiguous estimate of target speed emerges visual motion to eye motion is 100 ms, so that the first 100 ms if we normalize the weighted sum through division by the total of the pursuit response is driven only by visual motion and can activity across the population. Now, the estimate of target speed be considered as the ‘‘open-loop’’ response of the system. is equal to the actual target speed and is insensitive to the ampli- Therefore, we take the change in eye velocity in the first 100 ms tude of the population response. This way of estimating target of pursuit as an index of the estimate of target speed that speed for a population response is called ‘‘vector averaging’’ is driving pursuit, and we can think of the increased pursuit and is represented by the equation response as an illusion of increased target speed for degraded P motion. Figure 3D quantifies the illusion of increased target MTi PSi speed by plotting the normalized pursuit response as a function speed = i P (1) of the interval between flashes of the target moving in sampled MTi i motion (black symbols and lines). Pursuit’s estimate of target speed increases as a function of the temporal interval between target flashes up to an interval of 48 ms before declining at longer where MTi and PSi are the response and the preferred speed of intervals. Perception’s estimates show the same illusion the ith MT neuron. Salinas and Abbott (1994) showed that the use (Churchland and Lisberger, 2001).

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A C Figure 3. Pursuit Estimates Target Speed 100 by Computing the Center-of-Mass, or Smooth 15 Vector Average, of the Population 50 Response in Extrastriate Visual Area MT 10 (A) Schematic diagrams representing the speed 0 tuning curves of five selected MT neurons during 5 100 Smooth smooth target motion (blue) and target motion that Degraded Degraded has been degraded by flashing the target at sequen- 0 tial locations (red). Symbols indicate the responses 50 Eye velocity (deg/s) 0 100 200 300 400 500 600 of the five neurons to target motion at 20 /s. 0 (B) MT population responses synthesized by plot- Time from motion onset (ms) ting the responses of the five MT neurons in (A) as 0.25 1 4 16 64 256 1024 a function of their preferred speeds and fitting 150 Target speed (deg/s) D smooth curves. Arrows indicate the center-of- B mass or vector average of the two population 100 Population 100 responses. response (C) Average eye velocity as a function of time 50 during step-ramp pursuit from a monkey. Black 50 Data and red traces show responses to smooth and Decode of MT degraded target motion. 0 0 (D) Estimates of target speed as a function of the Pursuit response (%) Opponent neural response as percent of maximum 1 4 16 64 256 4 8 16 32 64 interval between flashes in the degraded, sampled target motion. Black symbols show measure- Preferred speed (deg/s) Motion sample interval (ms) ments made from the pursuit of a monkey, and red symbols show predictions made by using vector averaging to decode a model population response based on recordings from area MT. Data are replotted from Churchland and Lisberger (2001).

To our surprise, every MT neuron we recorded from showed The scenario outlined in Figures 3A and 3B is exactly what we a decreased response amplitude for the same degraded motion observed in the responses of MT neurons for smooth versus that created increased estimates of target speed (Churchland degraded target motion (Churchland and Lisberger, 2001). We and Lisberger, 2001). We inferred, therefore, that the population think that the effect was greater in neurons with lower preferred response was indeed smaller for degraded motion than for speeds because the smaller spatial limits of their receptive field smooth motion. This observation shows that smaller population mechanisms were exceeded at lower spatial separations in the responses do not necessarily lead to estimates of lower target degraded target motion. When we used vector averaging to speeds and contradicts the predictions of any decoding compu- decode MT population responses for target motions of the tation that uses the amplitude of the population response alone same speed but different intervals between flashes, we found to estimate speed. an effect that paralleled the illusion in pursuit perfectly Figure 3 shows how the population response for degraded (Figure 3D, red symbols and lines). We conclude that the illusion motion can have a decreased amplitude but still lead to esti- of increased target speed results from a systematic effect of mates of faster target motion compared to smooth motion. For degraded motion on the sensory population response in MT. smooth motion, assume that all five neurons in a model popula- The success of Figure 3 in accounting for the effects of apparent tion have peak responses of 100 (Figure 3A, ‘‘smooth’’). When motion stimuli on pursuit initiation makes us mindful that pursuit the target moves at 20/s, neurons with preferred speed close performance may be determined primarily by the sensory repre- to 20 will have close to maximal responses, and neurons with sentation of motion, a conclusion similar to those outlined by higher or lower preferred speeds will have smaller responses. Pack and Born (2001) for pursuit and in the work of Miles and We can view the population response (Figure 3B, blue symbols colleagues (e.g., Sheliga et al., 2008) for visually driven ocular and curve) by plotting each model neuron’s response as a func- following. tion of its preferred speed. With more model MT neurons, the For now, we regard vector averaging as a metaphor rather (mean) population response would be described by the blue than as a neural mechanism for estimating target speed. Indeed, curve, which has a peak of 100 for neurons with preferred it may be an imperfect metaphor because it does not account for speeds of 20. Decoding the population response with vector the relationship between MT responses and pursuit initiation for averaging would yield a speed estimate of 20/s. For degraded all target forms and contrasts (Krekelberg et al., 2006; Priebe and target motion, suppose that the reduction in tuning curve ampli- Lisberger, 2004). At the same time, an elegant experiment in the tude is greater for neurons with smaller preferred speeds saccadic system showed that vector averaging is an excellent (Figure 3A, ‘‘degraded’’). Now, creating the population metaphor for the process that decodes the population response responses by plotting each neuron’s response to target motion in the superior colliculus to control saccade direction and at 20/s reveals a smaller population response that is shifted to amplitude (Lee et al., 1988). Thus, vector averaging may be the right, toward higher preferred speeds (Figure 3B, red a useful and general way to think about neural population decod- symbols and curve). Decoding the population response with ing. We have found that a number of other mechanisms with vector averaging would yield an estimate that speed was faster potential neural implementations, such as statistically motivated than 20/s. computations (Deneve et al., 1999; Beck et al., 2008) and

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maximum-likelihood calculations (Jazayeri and Movshon, 2006), Small estimate target motion with means and variances quite similar to private noise Large those obtained from Equation 1. Therefore, we take advantage of Pursuit common noise Eye movement Equation 1 as a simple way to determine the center-of-mass of noise Visual Sensory the MT population response. At the same time, we leave input noise aside the unanswered, and in our view important, question of Perception Perception how the brain actually estimates target speed and direction noise from the population response in MT, or decodes any other neural population response for that matter. Smooth pursuit Vestibulo-ocular reflex 25 A B Sensory Sources of Movement Variation 20 We showed above that the mean MT population response predicts mean pursuit eye velocities for degraded, sampled 15 target motion as well as for normal, smooth target motion. 10 However, our analysis so far is based on mean responses of MT neurons and eye movements, and mean responses may 5 not reveal as much as we’d like about nervous system function. Eye velocity (deg/s) 0 To quote an unidentified meteorologist I heard on the radio quite -100 0 100 200 -100 0 100 200 a few years ago: ‘‘there is no such thing as a normal winter’s rain- Time from response onset (ms) fall, just an average winter’s rainfall, and that never happens.’’ The same is true of the brain. Neuroscientists frequently measure Figure 4. Comparison of Trial-by-Trial Variation in Pursuit average responses across repetitions of the same sensory stim- and Vestibulo-Ocular Reflex ulus, but the brain does not know about the mean responses of Schematic diagram summarizes possible sources of variation in estimates of neurons. It must act on the basis of the brief, single responses of target speed and direction for pursuit eye movements and motion perception. (A and B) Mean and variance of eye velocity for pursuit of target motion at 20/s many neurons for a single target motion. In the case of MT and (A) and for the vestibulo-ocular reflex evoked by head motion to the left at pursuit, action must be based on just a few action potentials in 20/s. Black curves show the mean eye velocity, and the gray shaded areas each of many MT neurons. Fortunately, at least under a limited indicate the trial-by-trial variance of eye velocity. set of stimulus conditions that are similar to those used for pursuit, MT neurons provide >80% of their maximal information is added downstream in the private components of the percep- about the direction of target motion within the first 100 ms of their tual or pursuit systems (Figure 4, schematic diagram). This response (Osborne et al., 2004). implies that the motor system downstream from the sensory Refocusing on the real problem solved by the brain raises the representation of target motion follows the sensory estimates key question of understanding trial-by-trial variations in pursuit of target speed and direction almost perfectly, even when those and neuron responses. Because neural responses are quite vari- estimates are erroneous or noisy. The work of Gegenfurtner et al. able, there is a risk that motor behavior also could be quite vari- (2003) does not agree totally with our conclusion of a shared able. In pursuit, this is true. In Figure 4A, the black line shows the noise source for perception and action, but a number of other mean pursuit eye velocity as a function of time, and the gray studies have postulated shared visual inputs for pursuit and ribbon indicates the impressive trial-by-trial variance. In perception (e.g., Stone and Krauzlis, 2003; Pack and Born, contrast, the eye velocity of the vestibulo-ocular reflex (VOR) is 2001). about seven times more precise for a similar mean eye velocity Neurons in MT, and most of the rest of the brain, are noisy— trajectory (Figure 4B). Because pursuit and the VOR use the the trial-by-trial variance of spike count is approximately equal same brainstem interneurons and extraocular motoneurons, to the mean spike count as expected for neurons with Poisson the variation in the initiation of pursuit cannot be attributed to spiking statistics. Why is this noise not eliminated when the brain noise added by the final motor pathways. averages across the many MT neurons that must be active for We think that the variation in pursuit results from sensory any target motion? The answer lies in noise correlations across errors in estimating target speed. In support of our view, quanti- MT. The spike counts of neurons with similar stimulus prefer- tative analysis showed that almost 95% of pursuit variation can ences tend to fluctuate up and down together from trial to trial be understood in terms of sensory errors in estimating the speed, (Zohary et al., 1994; Bair et al., 2001; Huang and Lisberger, direction, and time of onset of target motion (Osborne et al., 2009). Correlated fluctuations cannot be eliminated by averaging 2005). Further, the magnitude of the errors in estimating these across neurons and therefore have the potential to appear in the parameters for pursuit predicts an ability to discriminate ultimate behavioral output. between different directions or speeds of target motion that is If we make a model MT population response with the same only slightly larger than found in tests of perception (Osborne trial-by-trial variation in spike count found in MT neurons and et al., 2007). The similarity of the estimation errors for pursuit the same neuron-neuron correlations, then we find, indeed, and perception suggests that the variation in pursuit arises that the trial-by-trial variation in estimates of target speed are mainly from noise in the sensory representation in MT, in a pop- comparable to those found in pursuit behavior. In Figure 5A, ulation response that is shared by neural subsystems respon- we assemble population responses, defined as the response sible for perception and action. In contrast, perhaps little noise of all individual neurons in the population plotted as a function

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30 A 0.8 B p<0.05 Figure 5. Trial-by-Trial Variation in Pursuit Explained in Terms of Correlated Noise 0.4 across the Population Response in Sensory Area MT 0 (A) Model population responses obtained by pre- 20 R: MT-MT -0.4 senting a target motion at one speed and predict- ing the response of many neurons as a function of 04080120their preferred speed. Red curve shows the Diff. in preferred speed average across many repetitions of the same stimulus, and black curves show population responses # of spikes 10 0.8 C for individual target motions. (B) Neuron-neuron spike count correlations for 0.4 pairs of MT neurons as a function of the difference in the preferred speed of the two neurons in the Speed 0 variance 0 pair. Each symbol shows data from an individual 0.5 1 2 5 10 20 50 200 0.00.180.360.3 pair of neurons, and the red and black symbols flat show pairs with statistically significant versus Preferred speed (deg/s) R: MT-MT nonsignificant correlations. (C) Variance of estimates of target speed obtained by using vector averaging to decode model population responses under different assumptions about the magnitude and structure of neuron- neuron spike count correlations. Data have been simplified and replotted from Huang and Lisberger (2009). of their preferred speeds. As in Figure 3B, it is important to does so by taking into account the responses of all active remember that the curves in Figure 5A are not the tuning curves neurons. If the entire population code fluctuates up and down of individual neurons. As expected, the mean population in a correlated fashion, then the center of the population response (red curve) is smooth. In contrast, there is considerable response will not fluctuate, and the estimate of target speed trial-by-trial variation in population responses for individual will be very reliable. If, on the other hand, the spike counts of target motions (black curves), and this trial-by-trial variation neurons of similar preferred speeds fluctuate up and down leads to estimates of target speed that also vary from trial to trial. together while those of different preferred speeds fluctuate inde- If we had made a large population of model MT neurons with the pendently, then the center-of-mass of the population response variation of the individual neurons contrived to be independent of will be much more variable. all others, then the variation across the population response The analysis presented so far shows that the variation in esti- would average away, and the estimates of target speed (and mates of target speed and direction by pursuit are approximately pursuit) would be quite precise. However, Figure 5B summarizes the same in magnitude as those for perception and that the vari- our data (Huang and Lisberger, 2009) showing significant ation in the response of the correlated population of neurons in neuron-neuron correlation of spike counts in the first 150 ms of MT could be the source of the variation in pursuit. Further, for the response of MT neurons, the relevant time interval for pursuit. most target forms, the mean center of the MT population Importantly, the correlations have structure: pairs of MT neurons response is closely related to the mean estimate of target speed with similar preferred speeds were much more likely to show by pursuit. We suggest that most features of the initiation of statistically significant MT-MT correlations (red symbols), most pursuit can be explained by the mean, variation, and neuron- of which were positive. neuron correlations in the sensory population response in MT. Using Equation 1 to decode target speed from a realistic Of course, the correlated fluctuations in MT responses probably model population response that includes structured neuron- arise much earlier in the visual system, perhaps even in the neuron correlations reveals a single important principle. For large retina. However, their presence in MT seems to be sufficient to populations of neurons (here 4000), the variance of the esti- control the accuracy and precision of the initiation of pursuit. mate of target speed increases as a function of the peak MT- Indeed, it is not necessary to posit that any additional variation MT correlation in the model population, as long as the correla- is added to the initiation of pursuit downstream from the populations have the structure described by Figure 5B. For example, tion decoding step of sensory-motor processing. However, it the model with the peak correlation (0.36) that best described also is important to remember that we have analyzed only the our recordings from pairs of MT neurons produced an asymp- visually driven initiation of pursuit. Later in the pursuit response, totic variance of target speed estimate of 0.8 (Figure 5C). In real motor noise may accumulate far downstream, as postulated contrast, if the correlation between MT neurons lacks the struc- by Harris and Wolpert (1998). ture shown in Figure 5B but instead all pairs of MT neurons have So far, we have provided evidence that the mean and variance the same neuron-neuron correlation of 0.3, then the variances of of the initiation of pursuit can be assigned to the properties of the estimated target speed were little different from those for an sensory representation in area MT. We have suggested that the uncorrelated model MT population (R: MT-MT equals zero). downstream sensory-motor and motor circuits must be reacting This counterintuitive result can be understood by remembering reliably to the estimates of target speed and direction that that Equation 1 finds the center of gravity in terms of the emanate from MT. The next question is how pursuit is repre- preferred speed of the neurons with the largest response, but sented in different parts of the pursuit circuit and whether those

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Firing rate (% of max) Figure 6. Responses of Neural Populations at Different Levels in the Pursuit Circuit 0255075100 (A) Schematic diagram of the pursuit circuit. Rasters are taken from representative neurons recorded in MT, the FEFSEM, and the floccular A 100 FEFsem complex during step-ramp target motion. Each B raster was constructed from responses to many repetitions of the same target motion, and each Parietal line of the raster shows the response to one motion. MT FEFsem (MST?) (B and C) Color maps showing the time course of average firing rate for each individual neuron studied in the FEFSEM (B) and the floccular complex V1 0 (C). Each horizontal line of the color map shows the -100 100 300 500 time course for a different neuron, and the color Oculomotor Floccular scale shows the value of firing rate, normalized to Cerebellum Vermis complex 50 the peak for that neuron. LGN C Neurons in (B) and (C) were reported in Schoppik

Neuron number et al. (2008) and Medina and Lisberger (2007).

Brainstem 600 ms

0 -100 100 300 500 tify a region of strong activity during Time from motion onset (ms) pursuit (O’Driscoll et al., 2000). The floccular complex and FTNs have been impli- cated in motor learning in both the vestibulo-ocular reflex (Lisberger, 1994) and representations help us understand what neural computations pursuit (Kahlon and Lisberger, 2000; Medina and Lisberger, are performed in different areas. 2008, 2009), perhaps using similar mechanisms, while the oculomotor vermis plays an important role for learning in motor learning in saccadic eye movements (Soetedjo and Fuchs, Different Representations of Pursuit in the Cerebral 2006), suggesting that it could have a similar function for pursuit. Cortex versus the Cerebellum The textbook picture of the discharge of neurons in the pursuit The skeleton circuit diagram for pursuit eye movements (Lynch circuit beyond MT is that neurons in different parts of the circuit and Tian, 2006) appears in Figure 6A. From MT, motion signals transmit very similar neural signals. Indeed, in at least area MST are transmitted via a cortico-cortical pathway through the pari- (Newsome et al., 1988), the FEFSEM (MacAvoy et al., 1991), the etal sensory-motor cortex to the smooth eye movement region dorso-lateral pontine nucleus (Mustari et al., 1988), the cere- of the frontal eye fields (FEFSEM)(Stanton et al., 2005). There is bellar floccular complex (Stone and Lisberger, 1990), and the some evidence (Du¨ rsteler and Wurtz, 1988) but no definitive oculomotor vermis (Shinmei et al., 2002), many neurons show proof that area MST is the parietal area for pursuit. In other a response to visual motion at the initiation of pursuit and motor systems, the relationship between parietal neurons and some degree of sustained firing related to eye velocity during a specific movement has been established by the presence steady-state pursuit. During the latter, steady-state phase of of activity that builds up in the ‘‘delay’’ interval between presen- pursuit, all areas seem to combine signals related to (1) eye tation of a target and execution of a movement. In LIP and MIP, velocity in the head and (2) head velocity in the world to represent for example, delay activity occurs selectively for saccadic eye eye velocity in the world, called ‘‘gaze velocity’’ (Lisberger and movements and reaching movements, respectively, but not Fuchs, 1978; Thier and Erickson, 1992; Fukushima et al., 2000; for the other (Snyder et al., 1997). The recent development of Shinmei et al., 2002; Ono et al., 2004). The re-emergence of a delay period task for pursuit, and the demonstration of direc- the same basic signal throughout the circuit underscores the tion-selective delay period activity in the supplementary eye world coordinate system as the fundamental reference frame fields (Shichinohe et al., 2009), offers the chance for a similar used to compute signals that drive pursuit. The broad similarity identification of the parietal sensory-motor neurons for pursuit. of neural responses in different areas of the pursuit circuit could

From MT, the parietal sensory-motor cortex, and the FEFSEM, be related to the existence of recurrent loops from broad areas of signals are transmitted through a variety of brainstem relay nuclei the cerebral cortex through both the cerebellum and the basal to at least two regions of the cerebellum: the oculomotor vermis ganglia (Middleton and Strick, 2000). and the floccular complex (for review, see Lynch and Tian, 2006). The small rasters in the circuit diagram of Figure 6A provide The floccular complex can affect extraocular motoneurons via some details of the discharge of neurons in different parts of a disynaptic pathway that involves an identified group of ‘‘floccu- the pursuit circuit and illustrate one of the major processing tasks lar target neurons,’’ or ‘‘FTNs,’’ in the vestibular nuclei (Lisberger of the pursuit circuitry. The responses of neurons in area MT are et al., 1994; Scudder and Fuchs, 1992). In addition, the FEFSEM transient, as expected given the transient presence of visual provides a strong projection to the basal ganglia (Cui et al., image motion just before and during the initiation of pursuit 2003), where neurons have been found that discharge in relation (shaded area in Figure 1). The responses of Purkinje cells in the to pursuit (Basso et al., 2005; Lynch, 2009) and PET studies iden- cerebellum consist of a transient that is thought to arise from

Neuron 66, May 27, 2010 ª2010 Elsevier Inc. 483 Neuron Review

visual inputs (Miles and Fuller, 1975; Stone and Lisberger, 1990), for 50% of the variance, mainly because of the temporally followed by a sustained response that persists along with eye restricted responses and higher variation in the ﬁring rates of velocity when a target is stabilized with respect to the moving FEFSEM neurons (Schoppik et al., 2008). Therefore, we imagine eye during steady-state pursuit (Stone and Lisberger, 1990). that the FEFSEM works at a higher level to regulate pursuit in Recall that eye velocity also is sustained through an interval of a temporally selective fashion. We suggest that the FEFSEM image stabilization during steady-state tracking (Morris and Lis- divides the pursuit movement into small time epochs and allows berger, 1987). Thus, the transformation from sensation to action independent control of pursuit performance and/or learning in must include converting a transient sensory response in MT into each time epoch (Schoppik et al., 2008). sustained extraretinal signals that drive a sustained motor response. Variation, Signal, and Noise in the Motor Cortex Careful scrutiny reveals that the responses of neurons in the and the Cerebellum

FEFSEM (Schoppik et al., 2008) are more similar to those of floc- Our evaluation of the trial-by-trial variation in pursuit behavior cular neurons than of MT neurons but still differ in potentially suggested to us that we might learn more about neural process- important ways. The raster for one FEFSEM neuron in ing for pursuit by recording the neural and behavioral variation Figure 6A, for example, shows an early peak in firing related simultaneously during many repetitions of the same tracking to the onset of pursuit and a later peak during steady-state target motion. This is a different axis of analysis from the stan- tracking. The differences between floccular and FEFSEM dard approach of studying the time course of mean firing rates, responses are illustrated in Figures 6B and 6C, where the color and it provided two insights. First, it revealed another way in of each pixel indicates firing rate normalized for each neuron’s which responses of neurons in the floccular complex and the maximum, and each horizontal line shows the firing of FEFSEM differed and provided additional evidence that the two a different neuron as a function of time from 100 ms before structures have different roles in pursuit. Second, it provided to 500 ms after the onset of target motion. In effect, each hori- some unexpected insights into how signal and noise are pro- zontal line uses color to represent the peristimulus time histo- cessed in the brain. gram for a single neuron. Neurons are stacked from bottom We measured ‘‘neuron-pursuit correlations’’ by recording the to top according to the time when they reached 95% of their neural and behavioral responses to many repetitions of the peak firing rates. In the FEFSEM (Figure 6B), the onset of the same target motion and then computing the trial-by-trial correla- response occurs over the full time range of the movement in tion between firing rate and eye movement at every millisecond different neurons, and each neuron reaches a narrow peak at throughout the movement, for each neuron individually. For different times throughout the first 500 ms of target motion. example, Figure 7A summarizes the correlation between eye

Thus, different neurons in the FEFSEM appear to participate movement and firing rate for one Purkinje cell at one time during most strongly in pursuit for brief intervals that occur at different the initiation of pursuit. Each point plots data from an individual times during the movement, and the full population of neurons trial, showing a strong covariation of instantaneous firing rate tiles the entire movement time. In the floccular complex of the and eye velocity even though the visual stimulus was the same cerebellum (Figure 6C), in contrast, all Purkinje cells start to fire in each trial. Across time (Figure 7B), the time average of between 110 and 140 ms after the onset of target motion. They neuron-pursuit correlation for floccular Purkinje cells was close then reach broad peaks, mostly within the first 200 ms of to zero before the onset of pursuit, reached a peak of almost pursuit, and usually continue to fire throughout the pursuit 0.6, accounting for 36% of variance, during the initiation of movement. pursuit, and then settled to a lower, nonzero level during Differences in the relationship between firing rate and eye steady-state tracking. movement for the floccular complex versus the FEFSEM under- Comparison of the neuron-pursuit correlations recorded in the score likely differences in the function of the two areas during floccular complex and the FEFSEM revealed large, but quite pursuit. For floccular Purkinje cells, quantitative analysis of the different, neuron-pursuit correlations in the two structures dynamics of firing rate in terms of the parameters of eye motion (Figures 7C and 7D). Here, we show results from all individual reveals that >90% of the variance of each Purkinje cell’s average neurons instead of averages: each horizontal line in the color simple spike response as a function of time can be characterized map shows neuron-pursuit correlations as a function of time as a linear combination of eye position, velocity, and accelera- for an individual neuron. The lines for different neurons are tion (Shidara et al., 1993; Krauzlis and Lisberger, 1994; Hirata stacked vertically, ordered by the time at which 95% of the and Highstein, 2001; Medina and Lisberger, 2009). Analysis of peak correlation was reached. In the floccular complex the kinematics reveals that most PCs have preferred directions (Figure 7D), all Purkinje cells showed the same basic profile of aligned with the pulling directions of one pair of the extraocular neuron-pursuit correlation as a function of time. All reached muscles (Krauzlis and Lisberger, 1996). Thus, each Purkinje peak neuron-pursuit correlations about 100 ms after the onset cell participates throughout the pursuit eye movement and is of target motion. Many continued to show neuron-pursuit corre- concerned with dynamics and kinematics independent of time. lations later in the behavior, but at lower levels. In the FEFSEM We imagine that floccular Purkinje cells operate at a low level (Figure 7C), in contrast, different neurons reached their peak to generate the correct levels of muscular force in particular neuron-pursuit correlations at different times. Thus, the temporal muscles. In contrast, more complex models are needed to structure of neuron-pursuit correlations in the FEFSEM implies account for the relationship between the average firing of that each individual neuron makes its most important contribu-

FEFSEM neurons and eye movement, and the models account tion to pursuit in a speciﬁc, narrow time window, with the full

484 Neuron 66, May 27, 2010 ª2010 Elsevier Inc. Neuron Review

R: neuron-pursuit 0 0.2 0.4 0.6 10 deg/s 20 deg/s 180 1 30 deg/s A E 100 FEFsem C 0.5

130 variation 0 Downstream -100 100 300 500

"Eye velocity" 80 Time from motion onset (ms)

65 115 165 0 0 Firing rate (spikes/s) -100 100 300 500 F 0.5 0.2 50 Cerebellum 0.6 B D 1 Neuron number 0.4 2.5 0.1

0.2 5 Product of R's

0 0 10 0 Downstream variation (%) R: Neuron-behavior -100 100 300 500 -100 100 300 500 10 20 40 60 100 Time from motion onset (ms) Time from motion onset (ms) Pool Size

Figure 7. Variation, Signal, and Noise in the Pursuit Circuit (A) Correlation between eye velocity and instantaneous firing rate during the initiation of pursuit in one floccular Purkinje cell. Each symbol shows data from a different behavioral trial. ‘‘Eye velocity’’ is in quotes because the actual eye movement has been transformed into units of firing rate (see Medina and Lisberger, 2007). (B) Neuron-pursuit correlation as a function of time, averaged across all floccular Purkinje cells. Vertical dashed line shows the peak neuron-pursuit correlation during the initiation of pursuit. (C and D) Color maps showing the time course of neuron-pursuit correlations for each individual neuron studied in the FEFSEM (C) and the floccular complex (D). The color scale shows the value of the neuron-pursuit correlation coefficient, and each line shows the time course for a different neuron. (E) Variation predicted to be added to pursuit commands downstream from the floccular complex as a function of time. Curves with different rendering show predictions for different target speeds. (F) Results of a simple population model showing the relationship between variation added downstream, the size of the population, and neuron-pursuit correlations. The color scale plots the predicted product of neuron-pursuit correlations in pairs of neurons recorded simultaneously. Data are replotted from Medina and Lisberger (2007) and Schoppik et al. (2008). pursuit response tiled systematically by the full population of and Lisberger, 2007). We did so under the assumptions that

FEFSEM neurons. This supports our earlier suggestion that many (at least 1000) Purkinje cells were involved in the behavior each FEFSEM neuron could be controlling pursuit selectively at and that their signals were combined linearly at downstream its own specific time during the movement. areas. The calculation implied that all the variation in the first Twenty years ago, the sorts of neuron-pursuit correlations 100 ms of pursuit was present in the responses of the Purkinje illustrated in Figures 7C and 7D were unimaginable. We viewed cells, so that no noise was added downstream (Figure 7E). the variation in the nervous system as noise, and we assumed This is consistent with our hypothesis, based on behavioral that the noise was eliminated by averaging across neurons measures and recording from area MT, that the variation in with nominally similar tuning and independent noise. Thought pursuit initiation arises entirely from correlated noise in the changed gradually, starting with the work of Johnson et al. sensory representation of visual motion in MT. Later in the (1973) on the somatosensory system, the finding of Zohary pursuit response, however, our calculations implied that noise et al. (1994) that neurons in MT have correlated noise, and the is added downstream. As suggested by Harris and Wolpert observation of Britten et al. (1996) that one MT neuron could (1998) for noise of ‘‘motor’’ origin, the noise added downstream predict behavior (albeit weakly). Now it is clear that trial-by-trial from the floccular complex scales as a function of the magnitude fluctuations in neural responses are correlated across neurons, of eye speed (calculations for pursuit of three target speeds are that the presence of correlated noise limits the power of aver- shown in Figure 7E) and accumulates as a function of time. Thus, aging across neurons, and that the correlated noise will cause we suggest that there are two fundamentally different sources of variation in the resulting behavior (Shadlen et al., 1996). The pursuit variation, as there also seem to be for saccadic variation neuron-pursuit correlations illustrated in Figure 7 are less (van Beers, 2007). During the first 100 ms of pursuit, which is shocking in the modern context, and they turn out to allow a fairly driven by visual signals, variation arises from the sensory input. quantitative understanding of how signal and noise are pro- During the later, steady-state phase of pursuit, which is driven cessed in the pursuit sensory-motor system. by extraretinal signals, variation arises deep in the motor Knowing the variance of the behavior, the variance of the system. neural firing, and the neuron-pursuit correlation in the floccular For the FEFSEM, we obtained an intuitive understanding of the complex allowed us to calculate how much of the variation in processing of signal and noise through a simple model of pursuit was added downstream from the Purkinje cells (Medina population decoding. We created model FEFSEM populations

Neuron 66, May 27, 2010 ª2010 Elsevier Inc. 485 Neuron Review

Pursuit activation, motor attention, FEFSEM output Figure 8. A Gain, or Volume, Control that Modulates Visual-Motor Transmission for Pursuit The schematic diagram indicates that the pursuit Visual motion Motor command activation, motor attention, saccades, and the Volume (gain) control output from the FEFSEM are capable of adjusting the gain of visual-motor transmission. (A) Target motion used to demonstrate gain control. From top to bottom, the traces are super- Gain control during pursuit Gain control by micro-stimulation imposed eye and target position and superimposed eye and target velocity. Red and black 20 During pursuit show eye and target motion. The black arrow 10 A Eye C 10 points to the perturbation of target velocity, and Pos. 0 During fixation Target 0 the red arrow points to the eye velocity response, 20 100 ms later. The perturbation of target velocity caused a transient deﬂection of target position 10 that was too small to see on the low-resolution

Velocity 0 10 records in this panel. D Target (B) Black and purple traces show the time course 0 300 600 900 1200 0 of the eye velocity evoked by the same perturba- Time (ms) -10 tion of a moving target that was evoking pursuit 6 or of a stationary target that was used to maintain 3 B During pursuit Stimulation ﬁxation.

Eye/target velocity (deg/s) 3 0 No stim (C) Eye velocity evoked by a 75 ms electrical mi- 0 crostimulation in the FEFSEM. Black and red traces

Velocity During fixation -3 show responses to the same simulation during 0 100 200 300 400 -100 0 100 200 300 400 500 fixation versus during steady-state pursuit. Time relative to perturbation (ms) Time relative to stimulation (ms) (D) Eye velocity evoked by a brief perturbation of a stationary target that the monkey is fixating. Black and magenta traces show the responses in the presence and absence of concurrent electrical microstimulation in the FEFSEM. Data in (A) and (B) are replotted from Schwartz and Lisberger (1994). Data in (C) and (D) are replotted from Tanaka and Lisberger (2001). constrained to have the mean neuron-neuron correlation of than 100 neurons seem implausible, we suggest that very little 0.18 that we measured during pursuit. Then, we ran many variation is added downstream from the signals emanating simulated trials, computing the average ‘‘pursuit’’ commanded from the FEFSEM. by the model population and the ‘‘neuron-pursuit correlation’’ For both the cortex and the cerebellum, the analysis of neuron- for each unit in the model. To understand the performance of pursuit and neuron-neuron correlations leaves us with the inter- the model, we define the product of the neuron-pursuit correla- pretation that very little noise is added to the commands for tions for two neurons in a pair, which we will call RNB* and plot pursuit downstream from these areas. For the cerebellum, this as a color code in Figure 7F. Several principles emerge. (1) As statement is valid only for the initiation of pursuit, and for the the size of the pool of model neurons increases, moving along FEFSEM it is valid only for brief epochs that are different in each a horizontal line in Figure 7F, each neuron contributes less to neuron and that systematically tile the duration of the movement. the population drive for movement and the magnitude of It is actually quite remarkable that so little noise seems to be neuron-pursuit correlations decreases; so does RNB*. (2) For added to motor commands late in the process and quite startling large pools with no noise added downstream, shown in the that the motor system seems to be capable of following its upper-right corner of Figure 7F, the product of the neuron- sensory inputs with such high reliability. pursuit correlations of a pair of neurons (RNB*) approaches the theoretical limit of the value of the neuron-neuron correla- Between Sensory Representation and Action: Gain tions (0.18 in our data and simulations). For smaller pool sizes, Control and Target Choice

RNB* can be larger than the neuron-neuron correlations Pursuit operates on a very quick timescale. Only 100 ms elapses because few neurons are contributing. (3) As the amount of between the onset of target motion and the initiation of smooth variation added downstream increases, moving from top to eye motion. The short timescale tempts us to think of pursuit bottom along any vertical line in Figure 7F, the neuron-pursuit as a sensory-motor reflex. Yet, the richness of the pursuit circuit, correlation can explain less of the behavioral variance and its similarity to circuits for saccadic eye movements and arm therefore decreases, as does RNB*. In our data, RNB* was movements, and the temporal diversity of contributions from approximately equal to the neuron-neuron correlation and fell the FEFSEM belie such a simple view. Much happens between in the region indicated by the diagonal yellow stripe in sensation and action, even within the very short time that is avail- Figure 7F(Schoppik et al., 2008). This yellow stripe defines able. Figures 8 and 9 summarize some of the higher functions the possible combination of pool sizes and downstream noise that occur in relation to pursuit. that would be compatible with our observations of neuron- We (re)-discovered and codified modulation or ‘‘gain control’’ neuron and neuron-pursuit correlations. As pool sizes smaller (cf. Robinson, 1986; Luebke and Robinson, 1988) through the

486 Neuron 66, May 27, 2010 ª2010 Elsevier Inc. Neuron Review

Choose T1 Choose T2 tiny 10 Hz sine wave used to codify gain control in the first place. A T2 B T2 As shown in Figure 8D, perturbation of a stationary fixation target without activation of the FEFSEM evoked a very small smooth eye velocity (purple trace). During microstimulation of the FEFSEM (black trace), in contrast, the same perturbation of the fixation target caused a much larger smooth eye velocity response. We take this finding as evidence that activity emanating from the Eye Eye FEFSEM can control the gain of visual-motor transmission for pursuit. A role in gain control is entirely compatible with the

T1 T1 suggestion in a prior section that different neurons in the FEFSEM control pursuit in different brief time epochs during the movement. Indeed, the temporal specificity of the involvement of Figure 9. Gain Control Controlled by Saccade Execution as each individual neuron provides a mechanism for temporally a Mechanism of Target Choice for Pursuit specific control of the gain of visual-motor transmission for In each diagram, the black arrows indicate the orthogonal motion of two poten- pursuit (Tabata et al., 2008). tial tracking targets. The red circles indicate the initial position of fixation. The Several of our papers suggest that gain control is more than blue arrows indicate presaccadic pursuit in a direction that represents the vector average of the two target motions. The red dots indicate saccades to just arousal for smooth-pursuit eye movements. We suggest T1 in (A) and T2 in (B). The red arrows show postsaccadic pursuit in the direc- that it is a form of motor attention and can be applied differen- tion of the chosen target, even in the time immediately after the end of the tially to one of several moving targets to make choices about saccade, indicated by the ellipses. what objects to track (Schoppik and Lisberger, 2006; Garbutt and Lisberger, 2006; Gardner and Lisberger, 2001, 2002). An target motion outlined in Figure 8A(Schwartz and Lisberger, example appears in the cartoons of Figure 9. Here, two targets 1994). Here, a spot target started to move with a step-ramp are moving upward and to the right at the same time and the target motion. In half of the trials, the target underwent a pertur- monkey is allowed to track either one. Presaccadic pursuit bation consisting of a single cycle of a 10 Hz sine wave (black (blue arrows) takes the eye obliquely up and right in a direction arrow in Figure 8A). The perturbation evoked a clear response that represents the average of the two simultaneous target (red arrow in Figure 8A) if the perturbation was superimposed motions. Then, execution of a saccade to the target moving to on smooth target motion, but little or no response if superim- the right (A) or up (B) turns presaccadic pursuit that is averaging posed on a stationary fixation target (compare eye velocity (blue arrows) into postsaccadic pursuit (red arrows) that is selec- traces in Figure 8B). The behavioral paradigm was contrived so tive for the rightward or upward target motion (Gardner and that the image motion on the retina was the same during pursuit Lisberger, 2001). and fixation, while the state of the pursuit system was quite The emergence of target-selective pursuit immediately after different. We concluded, as illustrated in the schematic diagram the saccade (region within the ellipses in Figure 9) makes it at the top of Figure 8, that the pursuit circuit contains a gain, or impossible to invoke the use of visual inputs from the fovea as ‘‘volume,’’ control and that the gain is high when the subject is a mechanism of target selection; it would take 60–70 ms for tracking and low when the subject is fixating. We think that the visual inputs from the fovea to have their first effect on turning the gain control to ‘‘loud’’ is an essential step in initiating smooth eye velocity. Instead, we conclude that saccades are and maintaining pursuit of a moving target. As noted previously tightly linked to target choice for pursuit. Interestingly, saccades by Luebke and Robinson (1988), pursuit is not merely fixation evoked by microstimulation in the frontal eye fields or the supe- of a moving target: it is more. Pursuit, like saccades (Fischer rior colliculus have the same target-selective effect (Gardner and and Boch, 1983), requires activation and can proceed only after Lisberger, 2002), suggesting that execution of the saccade itself active release from fixation. can cause target choice. The schematic at the top of Figure 8 includes the output of the We think of target choice as an expression of voluntary control

FEFSEM as one of the variables that can control the gain of visual- of the gain of visual-motor transmission for visual signals arising motor transmission for pursuit. The evidence for including the at the speciﬁc spatial location of the target chosen for tracking.

FEFSEM, summarized in Figures 8C and 8D, comes from micro- Our hypothesis is that execution of a saccade is one way to stimulation in the FEFSEM (Tanaka and Lisberger, 2001, 2002). increase the gain of visual-motor transmission selectively for In these experiments, we introduced microelectrodes into the the target at the endpoint of the saccade. There also may be

FEFSEM and recorded until we found a site where neurons re- a mechanism that chooses targets in parallel for saccades and sponded selectively during pursuit and not during other kinds pursuit (Liston and Krauzlis, 2003), and humans are able to of eye movements. We then switched the electronics to allow bias pursuit toward one of two targets even without a saccade stimulation through the microelectrode. Stimulation with high (Garbutt and Lisberger, 2006). The target-selective nature of currents evoked an eye movement that was larger during pursuit gain control suggests that it can be understood as a form of than during fixation (Figure 8C, red versus black trace), consis- ‘‘motor attention’’ Schoppik and Lisberger (2006). As a homology tent with the possibility that the site of gain modulation for pursuit to the spatial search light theory of perceptual attention (Posner, is somewhere in the pathway from the FEFSEM to the motor 1980), there seems to be a spatial aperture for the visual inputs system. We then delivered stimulation at lower frequencies as that drive pursuit, and visual motion signals arising within that a stationary fixation point was perturbed briefly with the same aperture seem to obtain preferential access to the pursuit motor

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circuits (Schoppik and Lisberger, 2006; Garbutt and Lisberger, FEFsem 2006). The same concept helps to understand how the pursuit system can keep the moving eyes pointed at a moving target Image Eye: efference copy in spite of the potential drag provided by the oppositely directed Representation image motion from the stationary surroundings (Miles et al., of target 1991). Floccular complex

Roles for Efference Copy Efference copy is a general property of neural circuits in the . brain. For example, spino-cerebellar pathways include the E(t-Δt) ventral spino-cerebellar tract (VSCT), which appears to . assemble the same signals as are transmitted to motoneurons, E(t) and to send a surrogate of the motor command to the cerebellum . X + I(t-100) to assist in coordinating movement (e.g., Arshavsky et al., 1978). Gain control In the saccadic eye movement system, efference copy has been postulated to allow the brain to remember the location of future Figure 10. Multiple Functions for Efference Copy in the Pursuit targets in a spatial coordinate frame in the face of ongoing Circuit The arrows show the flow of neural signals. The open circles are summing saccades (Mays and Sparks, 1980). Part of the neural substrate junctions, and the circle with an ‘‘X’’ in it is a multiplication junction that imple- for this function exists in a pathway from the superior colliculus ments gain control. Blue and red arrows show the flow of efference copy through the thalamus to the saccadic regions of the frontal eye signals through the floccular complex of the cerebellum. Blue and magenta arrows show the flow of efference copy signals through the FEF . fields (Sommer and Wurtz, 2002). SEM Armed with an understanding of the multiple processes that operate to initiate smooth tracking, can we use the pursuit motion drives a change in smooth eye velocity (an eye accelera- system to understand how efference copy contributes to motor tion) that corrects the error between target and eye velocity control? In the pursuit system, efference copy signals are (Lisberger et al., 1981). Debate is not about the principle of using present almost everywhere. Efference copy signals in the pursuit efference copy to maintain eye velocity in the absence of image system are related to ongoing eye velocity and persist without motion but rather about the exact implementation in the brain. decrement when the eyes continue to move in the absence of Lesions of the floccular complex (Zee et al., 1981), oculomotor visual motion stimuli during steady-state pursuit. Signals that vermis (Takagi et al., 2000), MST (Du¨ rsteler and Wurtz, 1988), match these criteria have been recorded in Purkinje cells of the FEFSEM (Keating, 1991), and dorsolateral pontine nucleus (Ono floccular complex of the cerebellum (Stone and Lisberger, et al., 2003) cause eye velocity to lag behind target velocity 1990) and extrastriate visual area MST (Newsome et al., 1988) during steady-state pursuit, so all are potential substrates of and probably exist also in at least the FEFSEM, the caudate the extraretinal maintenance of steady-state pursuit. I favor nucleus, and the oculomotor vermis. a primary role for the cerebellum in this function (Lisberger, One hint of the function of efference copy in pursuit comes 2009), as shown by the red feedback pathway in Figure 10, from the observation that pursuit can maintain steady-state and I think that other areas support different components of tracking with eye velocity essentially equal to target velocity so pursuit (e.g., gain control, see below). Still, it is plausible that that there is next to no image motion (Figure 1A). If image motion the extraretinal maintenance of steady-state pursuit is shared, is removed during steady-state tracking by stabilizing the target with different sites contributing in different ways and on different on the moving eye (Morris and Lisberger, 1987), or if the target is timescales (e.g., Newsome et al., 1988). As an alternative to blinked as if it went behind a tree (Newsome et al., 1988; a direct role in maintaining steady-state pursuit, Komatsu and Churchland et al., 2003), then the eye continues to move Wurtz (1988) pointed out that the corollary discharge in MST smoothly without a decrement in speed. Thus, as noted initially could support the perceptual phenomenon that a target is still by Yasui and Young (1975), even small visual motion inputs perceived as moving even during pursuit that is good enough cannot explain the maintenance of eye velocity nearly equal to to eliminate visual motion inputs. target velocity during steady-state pursuit. As mentioned earlier An efference copy signal related to eye motion seems essen- in this paper, we cannot invoke the mechanical forces of inertia tial for maintaining a high gain of visual-motor transmission and momentum. Left to itself with constant innervation, the during pursuit, perhaps through the component of discharge in eyeball would slow to a stop within a few hundred milliseconds. the FEFSEM that is related to smooth eye velocity. Even though We think of the maintenance of eye velocity during steady-state credible image motion is the first impetus for increasing the pursuit as a housekeeping function that the pursuit system gain of visual-motor transmission and initiating the transforma- performs automatically, without voluntary intervention. tion of fixation into pursuit, image motion becomes small and The current theory of the maintenance of pursuit postulates unreliable during steady-state pursuit. Visual signals alone could that the command for eye velocity at any given time becomes not keep the gain of visual-motor transmission high. Figure 10 an efference copy that is fed back and becomes part of the proposes that eye motion signals provided by an efference command for eye velocity in the immediate future. Then, eye copy (magenta feedback pathway) would keep the gain of velocity is maintained automatically unless there is a difference visual-motor transmission high so that the system can be between target and eye velocity. As a consequence, any image responsive to any new visual inputs throughout the course of

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a single movement (see Figures 8A and 8B). Operationally, the Carey, M.R., Medina, J.F., and Lisberger, S.G. (2005). Instructive signals for brain is combining image and eye motion signals to create motor learning from visual cortical area MT. Nat. Neurosci. 8, 813–819. a representation of target motion that is present throughout the Chance, F.S., Abbott, L.F., and Reyes, A.D. (2002). Gain modulation from initiation and steady-state intervals of pursuit, whether or not background synaptic input. Neuron 35, 773–782. the target motion is causing image motion. Churchland, M.M., and Lisberger, S.G. (2000). Apparent motion reveals two components of pursuit eye movements with separate temporal and spatial Concluding Thoughts requirements. J. Neurophysiol. 84, 216–235. Pursuit eye movements are particularly important to us as Churchland, M.M., and Lisberger, S.G. (2001). Shifts in the population primates with foveal vision, because they allow us to keep our response in visual area MT parallel perceptual and motor illusions produced fovea pointed at objects that are moving. In addition, pursuit is by apparent motion. J. Neurosci. 21, 9387–9402. an interesting system to study because of its accessibility to Churchland, M.M., Chou, I.H., and Lisberger, S.G. (2003). Evidence for object detailed physiological and behavioral analysis and because of permanence in the smooth-pursuit eye movements of monkeys. J. Neurophy- siol. 90, 2205–2218. the possibility that interesting general brain functions such as population decoding, sensory-motor integration, and target Cui, D.M., Yan, Y.J., and Lynch, J.C. (2003). Pursuit subregion of the frontal choice can be studied rigorously within a known circuit. It seems eye field projects to the caudate nucleus in monkeys. J. Neurophysiol. 89, 2678–2684. likely that principles learned from the study of eye movements will generalize to other movements. In many ways, the problem Deneve, S., Latham, P.E., and Pouget, A. (1999). Reading population codes: solved by pursuit is similar to that solved when Barry Bonds hit a neural implementation of ideal observers. Nat. Neurosci. 2, 740–745. a home run or Roger Federer returns service; from sensation to Drew, T., Prentice, S., and Schepens, B. (2004). Cortical and brainstem control action in a very short time. The pursuit circuit parallels those of locomotion. Prog. Brain Res. 143, 251–261. for saccadic eye movements, reaching, and grasping; the circuit Dubner, R., and Zeki, S.M. (1971). Response properties and receptive fields of homology implies functional homology as well. Finally, pursuit cells in an anatomically defined region of the superior temporal sulcus in the implements voluntary features that are gracefully integrated monkey. Brain Res. 35, 528–532. into the quick sensory-motor response, features that seem likely Du¨ rsteler, M.R., and Wurtz, R.H. (1988). Pursuit and optokinetic deficits to be important for all kinds of movements. Thus, what we’ve following chemical lesions of cortical areas MT and MST. J. Neurophysiol. learned about pursuit so far, and what we’ll learn as a field going 60, 940–965. forward, seems likely to provide a broad understanding of how Fischer, B., and Boch, R. (1983). Saccadic eye movements after extremely sensation is converted to action, and what happens in between, short reaction times in the monkey. Brain Res. 260, 21–26. for all kinds of sensory modalities and motor acts. Fukushima, K., Sato, T., Fukushima, J., Shinmei, Y., and Kaneko, C.R. (2000). Activity of smooth pursuit-related neurons in the monkey periarcuate cortex ACKNOWLEDGMENTS during pursuit and passive whole-body rotation. J. Neurophysiol. 83, 563–587.

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