fundamental mechanisms of visual motion...

Progress in Neurobiology 68 (2003) 409–437

Fundamental mechanisms of visual motion detection:models, cells and functions

C.W.G. Clifforda,∗, M.R. Ibbotsonb,1

a Colour, Form and Motion Laboratory, Visual Perception Unit, School of Psychology, The University of Sydney, Sydney 2006, NSW, Australiab Centre for Visual Sciences, Research School of Biological Sciences, Australian National University, Canberra 2601, ACT, Australia

Received 8 May 2002; accepted 12 November 2002

Abstract

Taking a comparative approach, data from a range of visual species are discussed in the context of ideas about mechanisms of motiondetection. The cellular basis of motion detection in the vertebrate retina, sub-cortical structures and visual cortex is reviewed alongsidethat of the insect optic lobes. Special care is taken to relate concepts from theoretical models to the neural circuitry in biological systems.Motion detection involves spatiotemporal pre-filters, temporal delay filters and non-linear interactions. A number of different types ofnon-linear mechanism such as facilitation, inhibition and division have been proposed to underlie direction selectivity. The resultingdirection-selective mechanisms can be combined to produce speed-tuned motion detectors. Motion detection is a dynamic process withadaptation as a fundamental property. The behavior of adaptive mechanisms in motion detection is discussed, focusing on the informationalbasis of motion adaptation, its phenomenology in human vision, and its cellular basis. The question of whether motion adaptation servesa function or is simply the result of neural fatigue is critically addressed.Crown Copyright © 2003 Published by Elsevier Science Ltd. All rights reserved.

Contents

1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4102. General motion detector mechanisms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410

2.1. Fundamentals of motion detection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4102.2. Pre-filtering. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 410

2.2.1. On- and Off-channels. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4102.2.2. Temporal characteristics of pre-filters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412

2.3. Temporal delay filtering. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4132.4. Non-linear interactions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415

2.4.1. Facilitation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4162.4.2. Inhibition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4172.4.3. Speed-tuned motion detectors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 418

3. Evidence for the cellular mechanisms of motion detection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4193.1. Retinal motion detectors in vertebrates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4193.2. Sub-cortical motion processing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4213.3. Cortical motion processing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4223.4. Motion detectors in insect optic lobes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424

Abbreviations:AOS, accessory optic system; APB, 2-amino-4-phosphonobutyric acid; DAE, direction aftereffect; DS, direction selective; DTN, dorsalterminal nucleus; fMRI, functional magnetic resonance imaging; GABA,�-aminobutyric acid; ISI, inter-stimulus interval; LGN, lateral geniculate nucleus;LTN, lateral terminal nucleus; MAE, motion aftereffect; MST, medial superior temporal area; MT, middle temporal area (V5); MTN, medial terminalnucleus; NOT, nucleus of the optic tract; PMLS, posteromedial lateral supersylvian area; RGC, retinal ganglion cell; STOLF, space–time oriented linearfilter; TFRF, temporal filter response function; V1, primary visual cortex (area 17); V5, middle temporal area (MT); WIM, weighted intersection model

∗ Corresponding author. Tel.:+61-2-9351-6810; fax:+61-2-9351-2603.E-mail addresses:[email protected] (C.W.G. Clifford), [email protected] (M.R. Ibbotson).1 Tel.: +61-2-6125-4118; fax:+61-2-6125-3808.

0301-0082/03/$ – see front matter Crown Copyright © 2003 Published by Elsevier Science Ltd. All rights reserved.doi:10.1016/S0301-0082(02)00154-5

410 C.W.G. Clifford, M.R. Ibbotson / Progress in Neurobiology 68 (2003) 409–437

4. Adaptive mechanisms in motion detection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4264.1. Perceptual consequences of motion adaptation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4264.2. Function or fatigue?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4264.3. Informational basis of motion adaptation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4274.4. Dynamics of motion adaptation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4284.5. Directionality of motion adaptation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4294.6. Distinguishing motion adaptation from contrast adaptation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 430

5. Concluding remarks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 431

1. Introduction

While numerous visual animals lack color or binocularvision, the ability to see motion is ubiquitous and, nextto the detection of light and dark, may be the oldest andmost basic of visual capabilities (Nakayama, 1985). Conse-quently, visual motion processing is of fundamental interestto systems neuroscience and has been the subject of in-tense research. The present paper reviews recent advancesin our understanding of motion detection in biologicalsystems in the context of the large body of work that hasgone before. In particular, we emphasize the contributionmade by comparative studies of motion detection to thebroader understanding of the topic. The modern theoreticalframework for motion detection was developed from behav-ioral experiments on theChlorophanusbeetle (Hassensteinand Reichardt, 1956; Reichardt, 1961). The relevance ofthis early work to subsequent studies of motion detection,including primate cortical physiology and human psy-chophysics, indicates the importance of a broad biologicalapproach. A key feature of motion processing to emerge atboth the cellular and systems levels is its dynamic natureand adaptive plasticity. Consequently, motion adaptationwill be a major focus of this review. Perhaps the next greatchallenge in understanding motion detection is to reconcilethe wide range of theoretical approaches with the cellu-lar basis. Given the important advances that have alreadybeen made in this direction, we review progress on both ofthese levels.

The moving world is projected onto the retina in the formof a spatiotemporal pattern of light intensity. From this dy-namic signal, recovery of the direction of image motion isthe first stage of extracting behaviorally relevant informa-tion. Section 2of this review will cover motion processingfrom initial non-directional filtering strategies up to thepoint where a directional signal is produced.Section 3thendeals with the evidence for cellular mechanisms that mightperform these tasks in a range of brain areas and species.Specifically, we look at motion processing in the vertebrateand insect visual systems.Section 4deals with the im-portant role performed by adaptive mechanisms in motionprocessing.

2. General motion detector mechanisms

2.1. Fundamentals of motion detection

Exner (1894)was the first person to discuss the require-ments necessary for generating a motion signal from neuralcircuitry. He presented a drawing of a neural network thatcan be regarded as the first attempt at a motion detectormodel (Fig. 1A). However, it was another German scientist,Reichardt (1961), who promoted the first computation-ally based model of motion detection (Hassenstein andReichardt, 1956), a model that has subsequently been givenhis name (Fig. 1B). Although motion detector models varyin their detailed structure, the Reichardt detector is usefulin setting out the basic framework necessary for motiondetection (e.g.Borst and Egelhaaf, 1989).

Detecting the direction of motion requires that the im-age be sampled at more than one position or spatial phase,that these samples be processed asymmetrically in time, andthat they be combined in a non-linear fashion (Poggio andReichardt, 1973; Borst and Egelhaaf, 1989). This is a se-rial process that involves computation at multiple synapticlevels. These stages will be covered in three sub-sections:pre-filtering, delay filtering and non-linear interactions.

2.2. Pre-filtering

While Reichardt’s (1961)original model included spatialand temporal pre-filters (Fig. 1B), many subsequent mod-els of motion detection have neglected the importance ofpre-filtering (although seevan Santen and Sperling, 1984,1985; Ibbotson and Clifford, 2001a,b). Pre-filters are impor-tant because their properties affect the tuning characteristicsof the motion detectors they feed. What do we know aboutthe pre-filters of biological motion detectors?

2.2.1. On- and Off-channelsIt is well established that vertebrate photoreceptors are

hyperpolarized by light and their outputs are fed into bipo-lar cells. Sign conserving synapses feed Off-bipolar cellswhile sign inverting synapses feed On-bipolar cells (Werblinand Dowling, 1969) such that On-cells are excited only

C.W.G. Clifford, M.R. Ibbotson / Progress in Neurobiology 68 (2003) 409–437 411

Fig. 1. Two proposed “delay and compare” schemes for motion detection. (A) Schematic of neural “center for motion perception” proposed byExner(1894). Retinal fibers feed into pointsa–f and similar points. Signals from these points are summed at sitesS, E, Jf andJt . The time taken for a signalfrom a given point to reach a site of summation is proportional to the distance that signal must travel. It is this delay between signals from differentretinal locations that introduces directionality into the scheme. (B) Schematic of the mathematical model proposed byReichardt (1961)to describe theoptomotor response to motion stimuli in theChlorophanusbeetle. OmmatidiaA andB are separated by an angular distance,�s. The temporal responses,LA and LB, from these receptors are linearly transformed by the unitsD, F and H and linked together in the multiplier units,MA and MB. The outputsof the multiplier units are passed through low-pass temporal filters,SA andSB, and then subtracted from each other. The output of the subtraction stagecontrols the motor response of the beetle.

by brightness increments (On stimuli) while Off-cells areexcited only by brightness decrements (Off stimuli). Justas the On- and Off-cells are excited by opposite bright-ness polarities, they are also inhibited in a polarity depen-dent fashion, so On-cells are inhibited by Off stimulationand Off-cells by On stimulation (e.g.Enroth-Cugell andRobson, 1966; Hochstein and Shapley, 1976). Since On-and Off-cells are inhibited by brightness decrements and in-crements, respectively, why are both On- and Off-channelsnecessary when a single channel might suffice to carry infor-mation about both brightness polarities?Schiller et al. (1986)suggest that coding information about both increments anddecrements through opposing excitatory processes is muchmore efficient than using excitation and inhibition withina single channel. They reason that the low spontaneousactivity of retinal ganglion cells (RGCs) means that in-hibition below this level cannot represent much informa-tion, while a higher spontaneous rate would have a highmetabolic cost (Laughlin et al., 1998). The coding of bright-ness through On- and Off-channels might thus be the mostefficient way to satisfy both informational and metabolicconstraints.

How do signals from the On- and Off-channels interactin the generation of direction-selective responses? A classicexample of the way that pre-filtering affects the outputs ofdirectional neurons comes from rabbit retina, where twodistinct types of direction selective (DS) retinal ganglioncells have been identified: On-DS cells and On–Off-DS cells(Barlow et al., 1964; Barlow and Levick, 1965). Both celltypes generate direction-selective responses. On-DS cellsrespond to the movement of bright bars while On–Off-cellsrespond to the movement of both bright and dark bars.

Systems in other species show qualitatively different in-teractions between On- and Off-channels. For example, DScells in the pretectal nucleus of the optic tract (NOT) of themarsupial wallaby (Ibbotson and Clifford, 2001a), the pri-mary visual cortex of the cat (Emerson et al., 1987, 1992)and macaque middle temporal area (Livingstone et al.,2001) show interactions between On- and Off-signals thatutilize the sign of the incoming signals. In the insect visualsystem, the retinal image is not segregated into On- andOff-channels and wide-field direction-selective neurons inthe fly optic lobe receive input from motion detectors whosepre-filters maintain brightness polarity (Egelhaaf and Borst,


Fig. 2. Stimulus sequences and space–time plots of “phi” and “reverse-phi”motion. (Top left) Frames of an image sequence taken at timesT1–T5show a white bar moving across a gray background at constant speed. Seenin succession, the image sequence gives rise to the perception of “phi”apparent motion (Wertheimer, 1912), the bar appearing to move across thebackground. The sequence of frames may be placed together to form animage volume (top right), with time as the third dimension. (Bottom left)A slice through this space–time volume illustrates the fact that motion isequivalent to space–time orientation. (Middle row) A sequence of imagesin which the contrast polarity of the bar reverses between black and whiteeach time it moves. This image sequence gives rise to the percept ofreverse-phi motion in the direction opposite to the displacement of thebar (Anstis, 1970; Anstis and Rogers, 1975). (Bottom right) Space–timeplot of the reverse-phi motion stimulus.

1992). DS cells with inputs whose sign preserves brightnesspolarity show characteristic responses to apparent motionstimuli consisting of increases or decreases in brightness.Sequences of brightness steps of like polarity (either in-crements or decrements) elicit positive motion-dependentresponse components to motion in the preferred directionand negative responses to motion in the anti-preferred direc-tion. For sequences of opposite polarities, these directionalproperties are reversed (Fig. 2). These response propertiesare reminiscent of the reverse-phi phenomenon in humanvision (Anstis, 1970; Anstis and Rogers, 1975). For a widerange of spatial and temporal displacements, humans per-ceive sequential brightness changes at neighboring positionsin the visual field as motion in the direction of the secondbrightness change (“phi motion”:Exner, 1875). When thesequential brightness changes are of opposite contrast po-larity, motion in the reverse direction is perceived (“reversephi motion”: Anstis, 1970; Anstis and Rogers, 1975).

How are the On–Off interactions evident from psy-chophysics implemented in the primate visual system? Usingan On-channel blocking agent, 2-amino-4-phosphonobutyricacid (APB),Schiller (1984)showed the On-response in thesurround of lateral geniculate nucleus (LGN) Off-cells in theRhesus monkey is not affected by APB. This demonstratesthat the On- and Off-channels remain independent up to andincluding the level of the LGN. However, in cortical com-plex cells APB blocked the responses to moving dark/light(Off) but not light/dark (On) edges, suggesting that the

On- and Off-pathways converge at this level (Schiller,1992). Behaviorally, the detection of light increments butnot light decrements was severely impaired after injectionof APB into the vitreous of the monkey (Schiller et al.,1986). The responses of DS cells in the middle temporalarea (Zeki, 1974) and the medial superior temporal area(MST) of monkeys (Tanaka and Saito, 1989) have also beenshown to be independent of contrast polarity, consistentwith the notion that the On- and Off-pathways have alreadyconverged by this stage (seeSection 2.3).

For humans,Edwards and Badcock (1994)showed thatpsychophysical performance on a task requiring the globalintegration of local motion signals was similarly indepen-dent of contrast polarity. First, they found that the detectionof a global motion signal defined by a set of luminanceincrement (On) dots moving in a common direction wasimpaired equally by the addition of randomly moving“noise” dots of either contrast polarity. Second, they foundsub-threshold summation for global motion signals carriedby a mixture of luminance increment (On) and luminancedecrement dots (Off). Thus, it seems that the inputs toindividual motion detectors in the human visual systempreserve contrast-polarity, as evidenced by the reverse-phiphenomenon (Anstis, 1970; Anstis and Rogers, 1975), butthat local motion information from these detectors is inte-grated in a manner independent of the contrast-polarity ofthe original image signals (Edwards and Badcock, 1994).

2.2.2. Temporal characteristics of pre-filtersIf a motion detector received its inputs directly from

photoreceptors without any temporal filtering (Fig. 3), themotion detector output would be modulated by motionbut it would also respond strongly to a stationary image.Temporally band-pass filtering the image removes ongoingbrightness signals so that only changes in contrast enterthe detectors (Srinivasan et al., 1982). Temporal band-passfiltering can be implemented in a biological system by neu-rons with responses that are phasic. The impulse responsesof such neurons, defined as the response to a brief flash,consist of an initial excitatory phase followed by an in-hibitory period. The delayed temporal inhibition suppressesthe sustained response that would otherwise be generatedby a steady light. Neurons of this type respond primarilyto changes in light intensity while constant intensity lightproduces virtually no response. Motion detectors receivinginput from such band-pass filters will be tuned to respondselectively to temporal variations in the image rather thanits unchanging components. This selectivity comes entirelythrough pre-filtering strategies. It is also possible to reducethe response of the subsequent motion processing mecha-nism to unchanging image components through subtractionof the outputs of motion detectors tuned to opposite direc-tions of motion (seeFig. 1B).

Ibbotson and Clifford (2001b)found evidence that thepre-filters to the motion detectors feeding the mammalianpretectal nucleus of the optic tract adaptively match their


Fig. 3. Impulse response functions of (A and C) low-pass and (E and G) band-pass temporal filters and their corresponding temporal frequency responsefunctions. The first-order low-pass filter in (A) has an exponentially decaying temporal impulse response function. The filter in (C) is third-order low-pass,equivalent to a cascade of three of first-order filters. The impulse response in (E) is the temporal derivative of that in (C), while that in (G) corresponds tomodulating a third-order envelope with a sinusoid. (B and D) The filters with monophasic temporal impulse responses have low-pass temporal frequencyresponse functions. Cascading serves to narrow the pass-band (compare D with B). (F and H) The filters with temporally modulating impulse responsefunctions have band-pass temporal frequency response functions.

response properties to the prevailing visual environment. Inthat study, the response to two-frame apparent motion wasmeasured at a range of inter-stimulus intervals (ISIs) forstimulus contrasts of 20 and 80%. The stimulus consisted oftwo brief (10 ms) presentations of a sinusoidal grating sep-arated by a variable ISI. Apparent motion was produced bydisplacing the second grating by one-fourth of a cycle rela-tive to the first. For preferred-direction motion at the lowercontrast, the response to the second frame of the apparentmotion sequence was at least as large as the response to thefirst for all ISIs (Fig. 4). At the higher contrast, however,the response to the second frame was facilitated for shortISIs (10–50 ms) but attenuated for longer ISIs (50–700 ms).For ISIs between 50 and 700 ms, the response to the secondframe was actually facilitated by anti-preferred motion. Thisdependence of response sign on ISI duration is reminiscentof a range of apparent motion phenomena in human vision inwhich perceived direction of motion reverses for ISIs longerthan around 60 ms (Shioiri and Cavanagh, 1990; Georgesonand Harris, 1990; Pantle and Turano, 1992). Ibbotson andClifford (2001b) found that this behavior can be modeledby pooling the response of an array of elementary motiondetectors whose inputs preserve signal polarity and whosepre-filter characteristics depend on stimulus contrast suchthat pre-filtering is temporally low-pass at low image con-trasts and band-pass at high contrasts (Fig. 4).

Such a coding strategy would tend to reduce the transmis-sion of redundant information at high contrasts while max-

imizing signal strength at low contrasts (Srinivasan et al.,1982). Contrary to recent accounts (Strout et al., 1994;Johnston and Clifford, 1995a), these modeling results sug-gest that the dependence of perceived direction of apparentmotion on ISI in human vision might reflect the implemen-tation of a general purpose image coding strategy in earlyvision rather than a property particular to motion processing.

2.3. Temporal delay filtering

A temporal asymmetry is a necessary component of anydirection-selective motion detector (Borst and Egelhaaf,1989). This could come in the form of a temporal delayfilter (Reichardt, 1961) or in the form of a phase differ-ence between two temporally modulated filters (Adelsonand Bergen, 1985). Several attempts have been made tocharacterize the delay filters in biological motion detectors.Srinivasan (1983)used a stimulus in which a textured patternwas displaced a small distance in a single 5 ms frame. Thisimpulsive image displacement was used to stimulate DSneurons in the insect optic lobe. The stimulus displacementproduced an initial rapid increase in firing rate followedby an exponential decline in response level over the next3 s. The response waveform was referred to as an impulseresponse (not to be confused with the common name for anaction potential).Srinivasan (1983)was able to predict theresponse to continuous motion of the stimulus by convolut-ing the impulse response with the temporal profile of the


Fig. 4. Simulation of responses to two-frame apparent motion. The response of the temporal pre-filters is the sum of excitatory and inhibitory components.(A) The excitatory component (solid line) of the temporal impulse response has a shorter time constant than the inhibitory component (dashed line).(B) The contrast response functions of the excitatory and inhibitory components are shifted relative to one another such that the excitatory componentis responsive to lower contrasts. Simulated response of an array of correlation detectors with these pre-filters as a function of inter-stimulus intervalduration at stimulus contrasts of: (C) 80%; (D) 20%. Preferred, anti-preferred and non-motion conditions are represented by solid, dashed and dottedlines, respectively.

image motion. Several subsequent studies have used similarstimuli to record impulse responses of direction-selectiveneurons in insect and mammalian preparations (insects:deRuyter van Steveninck et al., 1986; Maddess and Laughlin,1985; Borst and Egelhaaf, 1987; mammals:Ibbotson andMark, 1996).

Although impulse responses proved useful in predictingthe shape of response waveforms to continuous motionstimulation under certain stimulus conditions, they failed topredict the temporal frequency response functions (TFRFs)of the neurons (Harris et al., 1999). The TFRF characterizesthe relationship between neuronal response magnitude andthe rate of temporal modulation in the moving stimulus. Theresults ofHarris et al. (1999)and those of previous studies(Zaagman et al., 1983; de Ruyter van Steveninck et al.,1986; Maddess, 1986; Borst and Egelhaaf, 1987; Ibbotsonand Mark, 1996) demonstrate that the time course of decayof the impulse response is strongly dependent on stimulushistory. It has been argued that this dependence might re-flect adaptation at the level of the motion detector delayfilter (de Ruyter van Steveninck et al., 1986; Clifford andLangley, 1996a; Clifford et al., 1997). However, subsequent

studies have failed to show a corresponding adaptive shiftin the preferred temporal frequency of the motion detectors(Ibbotson et al., 1998; Harris et al., 1999), which shouldremain inversely proportional to the length of the delay(Borst and Bahde, 1986; Egelhaaf and Borst, 1989; Cliffordand Langley, 1996a; Clifford et al., 1997), as illustratedin Fig. 5. These later studies suggest adaptation at thepre-filter level as a more likely substrate of the variationin impulse response decay time constant as proposed byMaddess (1986), althoughHarris and O’Carroll (2002)haverecently shown that variation in the impulse response decaytime constant can be modeled using fixed (non-adaptive)high-pass temporal pre-filters (seeSection 4.5).

Harris et al. (1999)attempted to characterize the im-pulse response of the motion detector delay filters feedingwide-field DS neurons in the insect optic lobes using an ap-parent motion stimulus. The stimulus consisted of two briefpresentations of a sinusoidal grating in which the secondpresentation of the grating was displaced by one-fourth ofa cycle in the preferred direction of the cell.Harris et al.(1999)plotted the magnitude of the response to the secondflash as a function of the ISI between flashes. The resultant


Fig. 5. Relationship between the decay time constant of the temporal impulse response and the peak of the temporal frequency response function. (A)Solid and dotted lines show impulse responses of first-order low-pass temporal filters with time constants in the ratio 2:1, such that the impulse responsedenoted by the solid line has the longer time constant. (B) Temporal frequency response functions of the same filters. The filter with the shorter timeconstant (dotted lines) has the temporal frequency response function that peaks at the higher temporal frequency. For time constants in the ratio 2:1, thepeak temporal frequencies are in the ratio 1:2.

“response-ISI” functions increased rapidly for ISIs up toaround 25 ms then decreased back to the size of the re-sponse produced by a single flash for ISIs of 200 ms ormore. Harris et al. (1999)argued that, if the grating pre-sentations can be considered as impulsive, the response-ISIfunction is equivalent to the impulse response of the delayfilter. Subsequently,Ibbotson and Clifford (2001b)mea-sured response-ISI functions for wide-field DS neurons inthe mammalian pretectum to the apparent motion stimulusdeveloped byHarris et al. (1999). Using computer simula-tions, Ibbotson and Clifford (2001b)demonstrated that theresponse-ISI function is, in fact, heavily dependent on thepre-filtering of signals prior to the motion detector. Only byincorporating temporal pre-filtering into their model wereIbbotson and Clifford (2001b)able to relate the response-ISIfunctions of neurons in the mammalian pretectal NOT totheir TFRFs. When this was done, it was often possible topredict the TFRF and peri-stimulus time histogram (PSTH)of a given cell from its response-ISI function to a reasonabledegree of accuracy. The latter study shows that, to measurethe temporal properties of the motion detector delay filter,it must be considered not in isolation but as part of a filtercascade that includes the temporal pre-filters.

2.4. Non-linear interactions

A linear combination of adjacent samples of the imagecan produce a difference in response modulation for thetwo directions of motion (Fig. 6; Jagadeesh et al., 1993,1997) but will not produce a directional time-averagedresponse (Watson and Ahumada, 1985). To producedirection-selective time-averaged outputs, a non-linearityis required. As a consistent directional time-averaged out-put is essential to signal the direction of motion, modelsthat include some form of non-linearity are used to modelthe responses of direction-selective neurons. The natureof the non-linear stage has received much attention fromtheoreticians and several models have been proposed (e.g.Barlow and Levick, 1965; Adelson and Bergen, 1985; van

Fig. 6. (Top) Space–time plot of a sinusoidal grating stimulus drifting atconstant velocity. (Middle) Variation about the mean level (dotted line)of the image signal at positionsx1 and x2. The two signals have thesame mean level, amplitude and temporal frequency but differ in temporalphase. (Bottom) Delaying one signal relative to the other shifts theirrelative temporal phase. For motion in the preferred direction, this resultsin constructive interference between the two temporal signals and a largevariation about the mean level. Motion in the anti-preferred direction putsthe two signals close to opposite temporal phases such that, in the sum,the variation about the mean level is small. Linear motion detectors ofthis kind are directional in their degree of response modulation but notin their mean response level.


Santen and Sperling, 1985; Egelhaaf et al., 1989; Amthorand Grzywacz, 1993; Johnston et al., 1992). We will nowdescribe the types of non-linear interactions that have beenconsidered and the evidence for their existence in biologicalsystems.

2.4.1. FacilitationConceptually, the simplest possible non-linear process

is a direct facilitatory interaction such as a multiplication(Fig. 1B). This type of interaction was first proposed byHassenstein and Reichardt (1956)in their Correlation model,subsequently referred to as the Reichardt detector, to accountfor behavioral data from theChlorophanusbeetle. Whilethere is no evidence to suggest that multiplication occurs at asingle synapse (Egelhaaf and Borst, 1992), non-linear facili-tation may arise through initial linear combination of signals(Watson and Ahumada, 1985) followed by a non-linear op-eration such as squaring (Adelson and Bergen, 1985), recti-fication (Mizunami, 1990), or thresholding (Jagadeesh et al.,1997). For example,Adelson and Bergen (1985)proposedan Energy model where signals from adjacent locations aresummed or subtracted. Such operations produce space–timeoriented linear filters (STOLFs), i.e. filters in which re-sponse latency varies systematically with spatial position.

Using a prime to denote a delayed signal, the combina-tions of inputs from adjacent locations,A andB, that pro-duce space–time oriented linear filters are:A − B′, A + B′,B+A′ andB−A′. As the combination is linear, a space–timeoriented linear filter will produce a response ifA or B isstimulated alone, with the latency of response depending onthe spatial position. Temporal coincidence is not requiredto generate a response from such filters. Conversely, mul-tiplication will only give a response if both input channelsare stimulated with an appropriate time interval. Thus, whiledirect (multiplication) and indirect (linear combination fol-lowed by squaring) mechanisms detect oriented structurein space–time, only the indirect models contain space–timeoriented linear filters (Fig. 7). The responses of such filtersdepend both on the direction-of-motion and the phase of theimage signal. The Energy model combines the squared out-puts of these filters to produce directional responses, referredto as motion energy. The response of the Energy model atthis stage depends on temporal coincidence and is indepen-dent of the phase of the image signal.

The responses of small-field DS neurons in the optic lobesof insects to moving sinusoidal gratings oscillate aroundthe mean response level at the fundamental and secondharmonic frequencies of the stimulus temporal frequency(DeVoe, 1980). Similar fundamental and second harmonicresponses can be observed in the responses of wide-fieldneurons if the gratings are presented in restricted areas ofa cell’s visual field (Egelhaaf et al., 1989; Ibbotson et al.,1991). The amplitude of oscillation at higher-order harmon-ics was found to be negligible, implying that a second-order(quadratic) non-linear interaction occurs in the elementarymotion detectors feeding into the neurons. Similar experi-

Fig. 7. Space–time oriented linear filters (STOLFs). Space–time plotsof STOLFs preferring motion (A) to the right, (B) to the left. Thepreferred speed and direction of each STOLF corresponds to its orientationin space–time. (C) Rightwards motion of a white bar across the graybackground produces a space–time trajectory matched to the space–timereceptive field structure of the STOLF preferring rightwards motion. (D)Rightwards motion of a contrast-reversing bar produces a space–timetrajectory better matched to the STOLF preferring leftwards motion,consistent with the perception of reverse motion.

ments on neurons in the mammalian nucleus of the optictract also revealed mainly fundamental and second harmonicresponses (Ibbotson et al., 1994), suggesting the operationof a quadratic non-linearity in the motion detectors feedinginto the NOT. To test this suggestionIbbotson et al. (1999);Ibbotson (2000)measured the responses of neurons in theNOT using apparent motion stimuli consisting of successivepresentations of identical contrast changes in two adjacentbars. Increasing the contrast of the bars increased responsemagnitudes in an approximately quadratic fashion up tocontrasts of 25%, supporting the notion of a facilitatorynon-linearity (Fig. 8). However, values beyond that con-trast led to a saturating response function, such that theoverall contrast response function had a sigmoidal appear-ance. These data emphasize that it is important to considerthe effect of other non-linearities in the system, such asspike generating mechanisms, in determining the measuredneuronal response, especially at high stimulus contrasts.

Since the multiplication stage of the Reichardt model andthe squaring stage of the Energy model are both forms ofquadratic non-linearity, it is difficult to distinguish direct andindirect models at the physiological and behavioral levels.For example, in their mathematically perfect forms, the En-ergy and Reichardt models produce identical outputs (vanSanten and Sperling, 1985; Emerson et al., 1992), sinceAB′= [(A + B′)2 − (A − B′)2]/4. However, biological systemsoperating according to these two principles are not indistin-guishable at all stages.Emerson et al. (1992)recorded theresponses of complex cells in cat striate cortex thought tobe elements within the sub-units of the Energy model. By


Fig. 8. Response vs. contrast function for an NOT neuron. Filled circlesshow the response to preferred direction apparent motion, stars showthe peak non-motion response, and open symbols show the response toanti-preferred apparent motion. The fitted curves are the best-fit quadraticfunctions for contrasts up to 25%.

recording the response to two bars displaced in space andtime and subtracting off the responses to the bars presentedindividually, Emerson et al. (1992)were able to calculatethe non-linear interaction of the two bars as a function oftheir spatial and temporal displacement. The data revealedspace–time oriented two-bar interaction fields that could besimulated by spatially integrating the outputs of motion en-ergy sub-units but could not be simulated by any stage ofthe Reichardt detector.

2.4.2. InhibitionIn the Reichardt and Energy models, the essential

non-linearity is facilitatory. To explain the responses ofOn–Off-DS retinal ganglion cells in the rabbit,Barlow andLevick (1965)proposed two mechanisms, a facilitatory andan inhibitory model. The facilitatory model was conceptu-ally similar to a single sub-unit in the Reichardt detector,while under the inhibitory model responses in one directionwere selectively inhibited. The inhibitory scheme has beenmodeled at the synaptic level using shunting inhibition(Torre and Poggio, 1978; Koch et al., 1983). Inhibition isgenerated by increasing the membrane conductance of aneuron and shunting incoming currents out from the cell.This interaction is division-like because shunting inhibitiondivides the excitatory currents by the membrane conduc-tance. The inhibitory model was tested by recording theresponses of On–Off-DS cells in the rabbit retina to appar-ent motion in the preferred (Amthor and Grzywacz, 1993)and null directions (Grzywacz and Amthor, 1993). Whilepreferred direction excitation produced linear facilitation,null direction inhibition appeared to be characteristic of

Fig. 9. Responses to apparent motion of On–Off-DS cells in the rabbitretina. In the following account, the stimulus consists of two adjacentbar stimuli (labeled slit-A and -B) placed in the cell’s receptive field andoriented perpendicular to the cell’s preferred direction. The cell’s preferreddirection is from slit-A to -B. In all cases, the expected response to thefirst slit has been subtracted. (A) Responses to apparent motion in thenull-direction. The upper curve shows the average response vs. contrastfunction generated when slit-A was presented alone (curve marked 0%).Other curves show the functions produced when the motion sequencewas slit-B then slit-A (slit-B contrasts are shown alongside the respectivecurves). As the contrast of slit-B increased, the response vs. contrastfunction produced by subsequent presentation of slit-A was more stronglyattenuated compared to stimulation of slit-A alone. (B) Response vs.contrast curves for apparent motion in the preferred direction. The dashedline shows the response function generated by stimulating slit-B alone.Other curves show functions obtained during apparent motion from slit-Ato -B (slit-A contrasts were 0, 10, 20 and 30%, as illustrated). Increasingthe contrast of slit-A additively enhances the response elicited by thesecond slit, so curves are shifted upwards in parallel. Adapted fromFig. 10of Grzywacz and Amthor (1993)and Fig. 10 of Amthor and Grzywacz(1993).

a non-linear division-like process similar to that expectedfrom the inhibitory scheme (Fig. 9).

Subsequently,Holt and Koch (1997) have shownthat shunting inhibition has a divisive effect only onsub-threshold excitatory post-synaptic potential amplitudes.Shunting inhibition actually has a subtractive effect on thefiring rate in most circumstances because the spiking mech-anism appears to clamp the somatic membrane potential toa level above the resting potential. Consequently, the cur-rent through the shunting conductance is independent of thefiring rate, which leads to a subtractive rather than a divi-sive effect.Holt and Koch (1997)suggest that observationof divisive inhibition in the spiking properties of cortical


neurons might be due to effects generated at the networkrather than the synaptic level. Given the complex circuitrythat forms the input to retinal ganglion cells in the rabbit(seeSection 2.1and Fig. 12), such network effects mightunderlie the division-like inhibition observed byGrzywaczand Amthor (1993). Direction-selective responses havebeen driven in rabbit On–Off-DS ganglion cells by edgesof light moving only 1.1�m (26′′ of visual angle) acrossthe retina (Grzywacz et al., 1994). This distance is smallerthan the spacing between rabbit photoreceptors, which isapproximately 1.9�m or 46′′ (Young and Vaney, 1991). Itis suggested that this directional hyperacuity is the result oflow-noise high-gain signal transmission from the photore-ceptors to the ganglion cells. Moreover, the result suggeststhat directional selectivity can be generated in small por-tions of the dendritic processes of ganglion cells and doesnot require a whole cell mechanism.

The intracellular mechanisms leading to the generationof direction-selective responses in rabbit On–Off-DS cellshave been studied using patch clamp recording (Taylor et al.,2000). Taylor et al. first showed that movement in the cell’spreferred direction caused a greater excitatory current toenter the cell’s dendrites. They then voltage-clamped thedendritic membrane at−70 and−30 mV and recorded thesynaptic currents produced by a moving bar. The differencebetween the synaptic currents generated by preferred andnull direction motion was more pronounced when the cellwas more depolarized (−30 mV) and was predominantlycaused by an increase in inhibition for null direction motion.When the intracellular concentration of chloride was equili-brated to the extracellular level, making the reversal potentialof �-aminobutyric acid A (GABAA) receptor-mediated inhi-bition equal to that of excitation, this difference disappeared.From this, it was concluded that a major component of thedirection selectivity of DS retinal cells in the rabbit is gen-erated by null-direction inhibition acting post-synapticallyto the ganglion cell dendrites (Taylor et al., 2000). How-ever, contrary to the findings for rabbit On–Off-DS cells,Borg-Graham (2001)found that DS retinal ganglion cellsin the turtle were not the site of the non-linear interactionand that direction-selective coding probably occurred earlierin the visual system. Details of motion processing prior toretinal ganglion cells in the turtle are given inSection 3.1(DeVoe et al., 1989). Borg-Graham (2001)questioned theassumption that chloride loading simply transforms all in-hibitory inputs to excitatory ones while leaving the originalexcitatory inputs unchanged. He suggested that the effectsof high intracellular chloride might be more complex, cast-ing doubt on the interpretation of ganglion cell dendrites asthe site of the non-linear interaction in the rabbit retina.

2.4.3. Speed-tuned motion detectorsIn a spatiotemporal frequency response profile, where

neuronal response is plotted as a function of spatial fre-quency on the abscissa and temporal frequency on the ordi-nate, there are diagonally oriented ridges of peak sensitivity

Fig. 10. Temporal frequency tuning vs. speed tuning. (A) Schematicspatio-temporal frequency response function for a speed-tuned neuron.Iso-response contours have their major axis lying along an iso-speed line(dotted). For all spatial frequencies, the peak response is obtained at thesame speed. (B) Schematic spatio-temporal frequency response functionfor a temporal frequency-tuned neuron. The spatio-temporal frequency re-sponse function is the product of separable spatial and temporal frequencyresponse functions. Iso-response contours have their major axis parallelto the spatial or temporal frequency axis. For all spatial frequencies, thepeak response is obtained at the same temporal frequency.

when a cell is speed tuned (Fig. 10A). The orientation ofthe ridge corresponds to a particular speed of image mo-tion. Alternatively, if the cell is not tuned to speed but ratherto specific spatial and temporal frequencies, as in the finaloutput of the Reichardt and Energy models, we would ex-pect a response profile with elliptical contours whose ma-jor axes are parallel to the spatial and temporal frequencyaxes (Fig. 10B). For a diagonally oriented ridge, space andtime are inseparable, meaning that the cell’s response profileis not simply the product of separate spatial and temporalfilters.

An alternative theoretical approach to motion detection isprovided by the gradient model (Fennema and Thompson,1978; Horn and Schunk, 1981; Srinivasan, 1990; Johnstonet al., 1992). Gradient-based approaches use filters that take“fuzzy derivatives” (Koenderink and van Doorn, 1987),blurring and differentiating the image, and combine the fil-ter outputs as a quotient of temporal and spatial derivativesto estimate velocity (Fennema and Thompson, 1978; Hornand Schunk, 1981; Johnston et al., 1992, 1999; Johnstonand Clifford, 1995a). Under this scheme, motion is com-puted from the ratio of temporal and spatial frequency-tunedchannels. The spatial and temporal frequency-tuned mech-anisms in the gradient model are non-directional but theircombination at the division stage produces a directional,speed-tuned response. The division operation can be thoughtof as a form of inhibitory non-linearity.

Directionality and speed-tuning emerge from the gradientscheme at a later stage than temporal frequency tuning. Thisis in contrast to the Reichardt model where speed-tuningat the sub-unit stage is replaced by temporal frequencytuning at the motion opponent stage (Zanker et al., 1999).Although a speed-tuned signal is available from the outputof a Reichardt sub-unit, the signal is superimposed on alarge non-motion related signal making it an impractical


site to extract speed information. The Energy model con-tains space–time oriented linear filters (Fig. 7) that are bothdirectional and temporal frequency-tuned (Emerson et al.,1992). Several models have been proposed to show howspeed could be extracted by analyzing the distributed outputof motion-energy sub-units tuned to selected spatial andtemporal frequencies (Heeger, 1987; Grzywacz and Yuille,1990; Simoncelli and Heeger, 1998). However,Ascher andGrzywacz (2000)point out that these models are oftenbased on rather theoretical filters that do not fit with exist-ing biological data.Ascher and Grzywacz (2000)present aBayesian model for the measurement of visual velocity thatallows the estimation of retinal velocity with more realisticassumptions about the form of the spatial and temporalfilters. Importantly, the model is consistent with observedaspects of speed perception such as the dependence ofperceived speed on contrast (Thompson, 1982).

Speed-tuning is a common property of direction-selectiveneurons in primate middle temporal area (Rodman andAlbright, 1987; Perrone and Thiele, 2001, 2002). A smallpercentage of direction-selective neurons have also beenidentified as speed-tuned in the mammalian pretectal nu-cleus of the optic tract (Ibbotson and Price, 2001) and inits avian homologue, the pretectal lentiformis mesencephali(Wylie and Crowder, 2000). Spatiotemporal receptive fieldprofiles in primate primary visual cortex (V1) are not tunedto image speed but rather to specific spatial and temporalfrequencies (Foster et al., 1985). Some V1 neurons havelow-pass (sustained) response profiles while others areband-pass (transient) (Foster et al., 1985; Hawken et al.,1996). Perrone and Thiele (2002)suggest that V1 neu-rons with separable response functions provide the inputto speed-tuned neurons in the middle temporal area. Theyprovide a model, the weighted intersection model (WIM),of how the visual system extracts speed independent ofspatial frequency. The WIM predicts that the maximumoutput of a speed-tuned middle temporal area (V5) (MT)neuron occurs whenever it receives equal input from sus-tained and transient V1 neurons. The WIM is related togradient schemes of motion detection in that the responseof its model MT neurons depends on the ratio of sustainedand transient input. However, while gradient schemes typ-ically compute image speed directly from the ratio of theresponses of transient and sustained mechanisms, the corre-sponding stage of the WIM model shows band-pass speedtuning consistent with the properties of MT neurons.

Much has been made in psychophysical circles of themathematical equivalence of the various classes of mo-tion detection scheme (e.g.van Santen and Sperling, 1985;Adelson and Bergen, 1986). For example, a least squaresgradient estimator of velocity based on Gaussian derivativefilters can be redescribed as an opponent Energy modelbased on filters oriented diagonally in space–time (Adelsonand Bergen, 1986), while Energy and Correlation modelswith the same constituent filters effectively perform the samecomputations in a different order (Emerson et al., 1992).

To identify decisively how image motion is computed ina particular biological system it is necessary to investigatethe component elements of the system (seeSection 3). Thedifferences between models in terms of directionality andtemporal frequency tuning mean that in principle they aredistinguishable physiologically. Of course, one problem withsuch an enterprise is knowing at which stage of the func-tional motion processing hierarchy you are recording. How-ever, given that gradient models have been shown to haveconsiderable predictive power in terms of the psychophysicsof motion perception in humans (Johnston and Clifford,1995a,b; Benton et al., 2000, 2001), more work is warrantedto investigate how such models might map onto the under-lying physiology. For example,Johnston et al. (1992)haveshown that stages of their Multi-channel Gradient model ofmotion perception respond to drifting sine wave gratings ina manner resembling that of some cortical simple and com-plex cells in terms of directionality and phase independence.

3. Evidence for the cellular mechanisms of motiondetection

3.1. Retinal motion detectors in vertebrates

The vertebrate retina contains all of the sequentialstages and lateral connections required for motion detec-tion (Fig. 11; Dowling, 1979), although these may not befully utilized in all species. The pathway begins with thephotoreceptors, then a layer of bipolar cells and finally theRGCs, which form the output of the retina (Fig. 11). At theinterface between the photoreceptors and the bipolar cells isa layer containing horizontal cells that provide the substratefor lateral interactions between neighboring areas of the vi-sual field. The horizontal cells provide the substrate for thelateral inhibition that generates center-surround interactionsand concentric receptive fields in bipolar cells and RGCs(e.g.Kuffler, 1953). More lateral interconnections are pro-vided by amacrine cells, which form a second horizontallayer at the interface between the bipolar and ganglion cells.

As outlined inSection 2.2.1, early recordings from therabbit retina revealed two types of direction-selective RGCs:On- and On–Off-DS RGCs (Barlow et al., 1964; Barlowand Levick, 1965). Both cell types were direction-selectivebut the On-cells responded only to the movement of brightedges while the On–Off-cells responded to the movementof both bright and dark edges. The discovery that someRGCs were directional provided an excellent opportunity touse the rabbit retina as a model system to investigate thecellular mechanism responsible for direction-selectivity in abiological system (for review seeVaney et al., 2001).

The On–Off-DS RGCs have a bistratified morphology(Amthor et al., 1984, 1989; Oyster et al., 1993). The outerdendritic stratum receives input from Off-center neurons (de-polarized by brightness decrements) and the inner dendriticstratum receives input from On-center cells (depolarized


Fig. 11. The retina has three nuclear layers: The outer nuclear layer (photoreceptors), the inner nuclear layer (bipolar, horizontal and amacrine cells) andthe ganglion cell layer (ganglions). Between the inner and outer nuclear layers is the outer plexiform layer where lateral connections are formed betweenphotoreceptors, bipolar cells and horizontal cell processes. Between the inner nuclear layer and the ganglion cell layer is the inner plexiform layer wherelateral connections are formed between bipolar, amacrine and ganglion cells. Information flows from photoreceptors to ganglion cells but there are alsomany lateral interactions.

by brightness increments). The dendrites of the On–Off-DScells co-stratify with cholinergic (starburst) amacrine cells(Vaney et al., 1989), which provide excitatory inputs to theganglion cells (Masland and Ames, 1976; Ariel and Daw,1982). The dendrites of the On-DS RGCs are monostrati-fied and reside in the same inner dendritic stratum as theOn-dendrites of the On–Off-DS RGCs (Amthor et al., 1989).The main difference between the dendrites of the two RGCtypes appears to be that the On DS cells have receptivefields that are approximately three times wider than theOn–Off-DS cells (Pu and Amthor, 1990). The On–Off-DScells do not have anatomical features that correlate withthe cells’ preferred response directions, but they do form avery dense tiling mosaic across the retina where cells of thesame type establish non-overlapping spatial domains (Oysteret al., 1993; Amthor and Oyster, 1995).

How do the structural elements discussed above fit intothe theoretical approaches discussed inSection 2? A se-ries of experiments has shown that amacrine cells appearto provide the neural substrate for some of the lateral in-teractions required for motion detection (Fig. 12). Morespecifically, starburst amacrine cells provide the substrateto potentiate the responses of ganglion cells to motion inall directions and possibly to generate preferred directionfacilitation (Grzywacz and Amthor, 1993; He and Masland,1997). Significant motion facilitation can arise from inputswell outside the region of retina occupied by the dendriticarborizations, which corresponds to the classical receptive

Fig. 12. Schematic of the neural circuitry thought to underliedirection-selectivity in the rabbit retina (adapted fromVaney et al., 2001).DS RGC, direction-selective retinal ganglion cell; Bip, bipolar cell; SA,starburst amacrine cell; GA, GABAergic amacrine cell. Note that there aremany cones feeding into each DS RGC, with much summation en route.This could give the impression that direction-selectivity is generated quitecoarsely within the receptive fields of the ganglion cells. However, edgesof light moving only 1.1�m, which is smaller than the inter-photoreceptordistance, can generate directional responses (Grzywacz et al., 1994). Thisresult suggests low-noise high-gain signal transmission from the photore-ceptors to the ganglion cells and the generation of direction selectivity insmall portions of the dendritic processes of ganglion cells (seeSection2.4.2).


field, of the On–Off-RGCs for preferred direction motionbut not for the anti-preferred direction (Amthor et al.,1996). Anti-preferred inhibition appears to arise from adifferent set of GABAergic amacrine cells, which againform synapses onto the DS RGCs (Fig. 12; Grzywacz et al.,1997; Massey et al., 1997). Consequently, motion in thepreferred and anti-preferred directions is actually driven bydifferent systems rather than identical systems with mirrorsymmetric directional tuning, as predicted by theoreticalmotion detectors such as the Reichardt and Energy models.Moreover,Section 2.3has already shown that the biophys-ical mechanisms underlying the response to motion in thepreferred and anti-preferred directions are different (Amthorand Grzywacz, 1993; Grzywacz and Amthor, 1993), i.e.preferred direction motion produces linear facilitation whilenull direction motion produces non-linear inhibition (Fig. 9).

The rabbit retina has provided much information about thecellular mechanisms of retinal motion detection but retinas inother vertebrate species have also yielded important results.Perhaps the most thoroughly studied example is the turtleretina (DeVoe et al., 1989). Although many results from theturtle retina provide evidence of a similar mechanism to therabbit (Marchiafava, 1979; Ariel and Adolph, 1985; Kittilaand Granda, 1994; Smith et al., 1996), intriguing differenceshave also been identified. Perhaps the most obvious is thatthe turtle retina has On–Off, On- and Off-DS cells, whilethe rabbit does not have the Off-specific type (Jensen andDeVoe, 1983).

DeVoe et al. (1989)showed in the turtle that 33% ofretinal ganglion cells were direction-selective. They alsoshowed that 37% of amacrine cells and 42% of bipolarcells were directional. The retinal ganglion cells were fullydirection-selective, giving spikes in one direction and nospikes in the opposite direction, suggesting strong non-linearmechanisms. In bipolar and amacrine cells, post-synapticpotentials were larger for movement in one direction thanthe opposite but the cells were not motion opponent. A smallnumber of directional turtle horizontal cells have been iden-tified (Adolph, 1988; DeVoe et al., 1989). They show farsmaller changes in amplitude between opposite directions ofmotion than are observed in bipolar, amacrine or ganglioncells.

Evidence for cholinergic and GABAergic processes inboth the inner and outer plexiform layer, combined withevidence of directional tuning even in the inner segmentsof a small number of cone-type photoreceptors (Carras andDeVoe, 1991), suggests that at least some directional codingoccurs very early in the distal retina of the turtle (Criswelland Brandon, 1992). It should be noted that the directionallybiased responses recorded in the cones of the turtle retina,as with the bipolar and amacrine cells, did not have the fulldirectional properties associated with DS retinal ganglioncells (Carras and DeVoe, 1991). Rather, responses to motionin one direction were simply larger than those to other di-rections (seeFig. 6), which corresponds to the expectationsof the early linear stages of certain theoretical motion detec-

tors such as the Energy model (Adelson and Bergen, 1985;Emerson et al., 1992). Anatomical evidence shows thatsome turtle cones have asymmetrically radiating teloden-dria (Ohtsuka and Kawamata, 1990), which could providethe spatial asymmetry required for early motion processingbetween photoreceptors at the level of the retina’s outerplexiform layer.

In summary, the rabbit retina has revealed a neural archi-tecture that contains bundles of DS ganglion cells that areinnervated by amacrine and bipolar terminals. This orga-nization provides all of the wiring required for calculatingthe direction of image motion. Perhaps most interesting isthe finding that the actual structure of the rabbit’s retinalmotion detectors does not correspond exactly with any ofthe theoretical models. Rather, evolution has developed asystem that utilizes all of the theoretical concepts describedin the models but is organized in a way not predicted by the-oreticians. This is certainly a lesson for physiologists whoattempt to find exact correlates of models in neural tissue.Rather, a broad approach is required that is guided but notdriven by the expectations of theoretical models. Anotherinteresting observation from the turtle retina is that the be-ginnings of directional tuning may occur as early as the dis-tal retina. Moreover, it is important to look for directionallybiased responses rather than motion-opponent responseswhen searching for the input structures of biological motiondetectors.

3.2. Sub-cortical motion processing

Although the lateral geniculate nucleus shows some direc-tional effects (Lee et al., 1979; Thompson et al., 1994; Whiteet al., 2001), the most striking regions of the sub-corticalmammalian brain in terms of direction selectivity are thepretectum and accessory optic system (AOS) (e.g.Simpson,1984). The pretectum contains the NOT and the AOS con-sists of three nuclei: the lateral, medial and dorsal termi-nal nuclei (LTN, MTN, DTN). In all of these nuclei, themost commonly encountered cells are those that respondin a highly direction-selective (motion-opponent) manner tothe movement of large regions of the visual scene. In mostcases, the cells give motion opponent responses in whichmotion in one direction excites the cell while motion in theopposite direction inhibits the cell’s spontaneous activity(Collewijn, 1975a; Hoffmann, 1989; Ibbotson et al., 1994).The NOT and AOS are connected to the motor system thatcontrols stabilizing eye movements such as optokinetic nys-tagmus (Collewijn, 1975b; Schiff et al., 1988; Belknap andMcCrea, 1988). The NOT and the nuclei of the AOS receivedirect input from the retina in all species studied (e.g. cat:Ballas and Hoffmann, 1985; monkey:Telkes et al., 2000). Insome animals, such as monkey and cat, there is also an in-direct input from the visual cortex (cat:Schoppmann, 1981;monkey:Distler et al., 2002) but in other species, such asthe marsupial opossum (Pereira et al., 2000) and wallaby(Ibbotson et al., 2002), there is no cortical input.


The motion opponent character of the responses in thesenuclei suggests that the action potentials are generated inthe cells after the final subtraction stage of the motionprocessing mechanism, as outlined inSection 2. This ob-servation leaves the possibility that the final subtractionphase actually occurs in the dendrites of the NOT and AOSneurons prior to the site of spike generation. The direc-tional responses in the NOT of the wallaby, which doesnot receive input from the cortex (Ibbotson et al., 2002),compare very well with the final subtraction stage of boththe Energy and Reichardt models (Ibbotson and Clifford,2001a,b; Ibbotson et al., 1994). It has also been establishedthat the fundamental non-linearity in the motion process-ing mechanism is quadratic, as predicted by both models(Section 2.4.1; Ibbotson et al., 1999). The spatiotemporalresponse properties of neurons in the sub-cortical motionprocessing areas of the avian brain suggest that similarmotion detector mechanisms are in operation (Wylie andCrowder, 2000). Indeed, the spatiotemporal tuning of cellsin the avian and mammalian brain are organized in a verysimilar fashion across the cell population, suggesting thatthe visual environment during head and eye movementsmolds the spatiotemporal properties of the neurons acrosswidely separated phyla (Ibbotson and Price, 2001).

The retinal ganglion cells that provide the input to theNOT and AOS in vertebrates are generally slowly con-ducting ganglion cells with small- to medium-sized cellbodies. These are so-called ‘specialized’ cells in primates(Telkes et al., 2000) and W-cells in cats, rabbits and rats(Ballas et al., 1981; Pu and Amthor, 1990; Kato et al., 1992;Rodieck and Watanabe, 1993). It is known that these cellsare direction-selective in cats (Hoffmann and Stone, 1985),rabbits (Oyster et al., 1972) and turtles (Rosenberg and Ariel,1991). Ilg and Hoffmann (1993)found that most corticalcells that could be stimulated anti-dromically from the NOTwere strongly directional. Therefore, the cortical input inprimates is also already direction-selective, as evidenced bythe strong input from directional fibers from the extrastri-ate motion processing areas MT and MST (Hoffmann et al.,2002). Presumably, the role of the NOT neurons is to sum-mate the inputs from directional cells to generate selectiveresponses to large field stimulation. Moreover, as many cellsfrom the visual cortex are not motion opponent, the NOTneurons might provide the neural substrate for the final sub-traction phase to produce motion opponency. The evidencesuggests that the cellular mechanisms of directional motiondetection occur primarily before the NOT or AOS. Most di-rectional properties probably arise in the retina but in cer-tain species there is an additional input from higher visualcenters in the cortex.

3.3. Cortical motion processing

The visual cortex is one of the most heavily studied areasof the brain. The majority of work on cortical motion pro-cessing has been conducted on cats (Hubel and Wiesel, 1962,

1965) and primates (Hubel and Wiesel, 1968; Foster et al.,1985). However, the physiology of cortical neurons has alsobeen studied in a number of other mammals such as the rab-bit (Murphy and Berman, 1979), opossum (Rocha-Mirandaet al., 1973) and wallaby (Ibbotson and Mark, in press). Thegeneral finding in all the species mentioned is that directionalresponses are common in the primary visual cortex (referredto as area 17 or area V1). Other areas in the visual cortex areknown to specialize in coding motion information, notablythe middle temporal area (MT or V5) in primates (Dubnerand Zeki, 1971) and the posteromedial lateral supersylvianarea (PMLS) in cats (Blakemore and Zumbroich, 1987).Cells fall into three main categories: (1) non-directional; (2)directionally biased (non-opponent); and (3) motion oppo-nent. Cells in the first category are not directional but maybe quite strongly orientation tuned, e.g. they might respondstrongly to vertically oriented gratings but not to horizontalgratings (Mazer et al., 2002). Orientation tuned cells usuallygive their best responses when an oriented bar or gratingis moved back and forth along an axis perpendicular to thepreferred orientation. The directionally biased cells tendto be orientation tuned but the response to motion in onedirection along the preferred motion axis (which is perpen-dicular to the preferred orientation axis) is stronger thanthe response in the opposite direction (Henry et al., 1974).Neurons with motion opponent properties respond stronglyin the preferred direction and are inhibited by motion in theopposite direction. It has recently been reported that, at highspeeds, many neurons in the primary visual cortex of catand monkey appear to be selective for motion parallel ratherthan perpendicular to their preferred orientation (Geisleret al., 2001). This has been taken as support for the hy-pothesis that spatial streaks caused by motion smear in theimage can also be used as a cue in the perception of motion(Geisler, 1999).

The majority of cells in the LGN, which is the primaryrelay between the retina and cortex, have responses that aresimilar to those of center-surround retinal ganglion cells(Hubel and Wiesel, 1961). Some LGN cells show weakorientation tuning and some directional effects (Lee et al.,1979; Thompson et al., 1994; White et al., 2001). However,as a general statement, the LGN contains cells that havemuch weaker orientation and directional preferences thanthe cortex. It is therefore reasonable to suggest that V1, orat least the interface between the LGN and V1, is a goodplace to look for the cellular basis of motion detection inthe geniculo-cortical pathway. However, before discussingthe evidence from the LGN and V1 themselves, it is worthconsidering evidence from higher areas of the cortex thatreceive input from V1.

The most striking region of the primate brain in terms ofmotion processing is the middle temporal area, MT or V5(Dubner and Zeki, 1971). The majority of cells in MT aredirection-selective but it does not appear to extract localmotion information itself but rather receives directional sig-nals from earlier stages in the visual system (Livingstone


et al., 2001). Lesions of V1 in primates greatly reduce theprevalence of DS cells in MT but do not totally abolishthe phenomenon (Rodman et al., 1989; Girard et al., 1992).MT also appears to receive directional inputs directly fromsub-cortical structures such as the colliculus and pulvinar(Rodman et al., 1990; Bender, 1982; Beckers and Zeki,1995). Movshon and Newsome (1996)recorded from V1neurons that could be anti-dromically activated by electricalstimulation of MT. They found that these cells were alreadydirectionally biased. The evidence therefore points towardsV1 as a major location for motion computation. It has beenproposed that directional V1 cells are local motion energyfilters (Adelson and Bergen, 1985; Heeger, 1987; Grzywaczand Yuille, 1990; DeValois et al., 2000). As outlined inSection 2, there is evidence to suggest that certain simpleand complex cells in cat V1 have directional propertiesthat are similar to those predicted by stages of the Energymodel (Emerson et al., 1992; Emerson, 1997; Emerson andHuang, 1997).

Livingstone et al. (2001)recorded from MT neuronsin primates and showed that directionality occurred forsequential presentation of stimuli less than 1/10 of a de-gree apart. This distance is far smaller than the receptivefield sizes of most directional V1 cells. The implicationis that interactions are most probably occurring betweensub-regions or -units within the receptive fields of V1 cells(Fig. 13). It was also established that responses of MT

Fig. 13. Schematic of the flow of information from lateral geniculatenucleus (LGN) to the middle temporal area (MT) via the primary visualcortex (V1). The LGN has lagged and non-lagged cells that feed intoV1 cells. In primate V1, non-directional lagged and unlagged cells havebeen identified (DeValois et al., 2000). In cat, cells with lagged andunlagged zones have been found (Saul and Humphrey, 1992). In primates,it would appear that V1 cells then feed into other V1 neurons such asspecial complex cells. However, the special complex cells may also receivedirect input from the LGN. By the time information reaches MT it hasbeen processed so that very specific movement information is integratedtogether.

cells to sequentially presented neighboring bars of oppositecontrast (i.e. light bar then dark bar) produced invertedresponses. This result, as discussed inSection 2.2, indi-cates that the polarities of the signals entering the motiondetectors are preserved. Retinal ganglion cells, LGN neu-rons and simple cells in the visual cortex show invertedresponses to opposite contrasts while complex cells in thecortex do not. Therefore,Livingstone et al. (2001)suggestthat direction-selectivity is generated within or betweengeniculate inputs or simple cells.Movshon and Newsome(1996)found that the V1 neurons that projected directly toMT were of the special-complex type, i.e. they respondedto a broad range of spatial and temporal frequencies andwere sensitive to very low contrasts. It is probable that thespecial complex cells receive their input from directionalsimple cells or directly from LGN fibers or both (Fig. 13).

Evidence suggests that the spatial separation betweeninputs arises from the differences in the receptive fieldlocations of neighboring LGN or simple cells.DeValoiset al. (2000)find that directional V1 cells in the macaquemonkey get the inputs required for motion detection bycombining signals from two identified sub-populations ofnon-directional cortical neurons. These sub-populationsdiffer in the spatial phases of their receptive fields. That is,both cell types have On and Off zones but correspondingzones are spatially displaced with respect to each other. Thetwo sub-populations also have distinct temporal properties:those with a slow monophasic temporal response and thosewith a fast biphasic temporal response. The fast biphasiccells cross over from one response phase to the reversejust as the monophasic cells reach their peak response.This 90◦ (quadrature) phase difference would make thetwo sub-populations of cells ideal building blocks for theEnergy model.

Where might be the origin of the temporal differencesin the non-directional V1 sub-populations identified byDeValois et al. (2000)? One possibility is that temporal dif-ferences arise from LGN neurons (Fig. 13). In the macaque,parvocellular LGN cells are slow and largely monopha-sic while magnocellular LGN cells are fast and biphasic,leading DeValois et al. (2000)to suggest that the twonon-directional cortical sub-populations they identify mightreceive their input from parvo and magno LGN cells, re-spectively. In the cat, X- and Y-relay cells in the LGN havebeen classified as lagged or non-lagged (Mastronarde, 1987;Mastronarde et al., 1991; Humphrey and Weller, 1988).When stimulating with sinusoidally luminance-modulatedstimuli, lagged and non-lagged cells fire about a quarter ofa cycle out of phase at low temporal frequencies (<4 Hz).Saul and Humphrey (1990)simulated the input of laggedand non-lagged cells onto cortical neurons and showed thatthe responses would be direction-selective at low temporalfrequencies but would lose that directionality at higher fre-quencies (>4 Hz).Saul and Humphrey (1992)went on touse sinusoidally luminance-modulated stimuli in cortex tosearch for signs of lagged and non-lagged inputs to cortical


neurons. They found lagged and non-lagged zones withinthe receptive fields of the cortical neurons. The distributionof latency and absolute phase across the sample of corticalsimple cells was similar to that found in the LGN. Thesimilarity between cortex and LGN was greatest in thegeniculate recipient layers of the cortex.

In conclusion, the results from various mammals indicatethat the cortex contains a plethora of cell types within thevery general categories of simple and complex cells. Thesmallest image displacement that leads to a directional re-sponse is considerably smaller than the receptive field size ofmost simple cells, suggesting that the essential lateral inter-actions may occur between the terminals of LGN neurons.Subsequent processing progresses from linear summation ofsignals in some LGN neurons up to highly directional re-sponses in the special complex cells that project to MT (inthe case of primates). It is clear that far less is known at thecellular level in the cortex than in the retina. However, thegeneral architecture of the cortical system is slowly beingrevealed.

3.4. Motion detectors in insect optic lobes

As alluded to inSection 2, a great deal of information re-lating to motion detection has arisen from work on insects,including the classic work ofHassenstein and Reichardt(1956). It is interesting to look at the mechanisms of motiondetection at the cellular level in insect optic lobes, whichare made up of three neuropiles (Fig. 14). Starting just be-low the retina and working inwards towards the brain, theseneuropiles are the lamina, medulla and lobula complex. Themost thoroughly studied insect visual system is that foundin flies (Douglass and Strausfeld, 2001) but the nervous sys-

Fig. 14. Schematic of connections in the insect optic lobes. All elements shown are thought to be involved in motion detection. PR, photoreceptors; L2and L4, two types of lamina monopolar cells; Am, lamina amacrine cells; T1, basket T-cells; Tm1, type 1 transmedullary cells; T5, bushy T cells; C2,type 2 centrifugal neurons. As in the vertebrate retina (Figs. 11 and 12), the interconnectivity between channels is very extensive.Gilbert et al. (1991)suggest that non-linear interactions occur at lamina-to-medulla connections, e.g. perhaps L2 to Tm1. The T4 cells are not shown in the diagram becausetheir role in motion processing (if any) is not well established. T4s have their dendrites close to the terminals of the intrinsic transmedullary cells (iTm)in the inner medulla and terminate in the lobula plate.

tems of other insects have also contributed to the field (e.g.bees:Ibbotson, 1991a; moths:Milde, 1993; locusts:Osorio,1986). The lobula complex in flies is divided into two com-partments. The most dorsal compartment is referred to asthe lobula plate and it contains direction-selective neurons(reviewed byHausen, 1993) and has been described as atectum-like structure (Douglass and Strausfeld, 2001). Theneurons in the lobula plate transfer information from the op-tic lobes into the midbrain and are involved in controlling op-tomotor responses. Optomotor responses are reflexive headand body movements that attempt to stabilize the retinal im-age and control body orientation during walking and flight.The lobula plate functions in a similar fashion to the nucleiof the AOS and pretectal NOT in mammals (seeSection 3.2).

Extensive studies on the neurons of the lobula plate showthat these cells have response properties very similar to thoseexpected from the final subtraction stages of the Reichardtand Energy models (Egelhaaf et al., 1989), as is the casein the AOS and NOT of mammals (Ibbotson et al., 1994;Ibbotson and Clifford, 2001a,b). That is, signals are alreadymotion opponent. Elementary motion detector units in thefly can be excited by stimulation of just two receptor cellsin adjacent facets of the eye (Kirschfeld, 1972; Franceschiniet al., 1989). The discrete nature of local motion interactionsin insects should assist in tracing movement signals at theelectrophysiological and anatomical levels. Given the clearresponse properties in the lobula plate and the highly repet-itive and organized nature of the medulla and lamina, it isinteresting to search for the elements of the optic lobes thatprovide the input to the lobula plate neurons. This search hasthe very real possibility of identifying the biological build-ing blocks of elementary motion detectors (DeVoe, 1980;DeVoe and Ockleford, 1976; Gilbert et al., 1991).


Movement-specific responses have not been identifiedfrom cells in the lamina (Mimura, 1974) but responses arehighly direction-selective in the lobula plate. The elementsresponsible for direction-selectivity must, therefore, be lo-cated in the circuitry between the output synapses of thelamina cells and the input synapses of the lobula plate. Inthe following account, we will start in the lobula plate (themost directional area) and move outwards towards the lam-ina (the least directional area). The dendrites of the lobulaplate neurons receive retinotopically organized synaptic in-puts from T4 and T5 neurons (Strausfeld and Lee, 1991).The T4 neurons have their dendrites in the medulla whilethe dendrites of the T5 cells reside in the outer stratum ofthe lobula (Fig. 14). It has been suggested that the T4 andT5 cells are functionally similar to retinal ganglion cells inmammals (Douglass and Strausfeld, 2001). The terminalsof the T4 and T5 cells in the lobula plate are located in fourmain levels that have distinct preferred motion directions.T5 cells generate motion opponent responses to movingpatterns that are similar to those of the lobula plate neurons(Douglass and Strausfeld, 1995). The receptive fields of T5cells are, however, far smaller than those of lobula plateneurons. In contrast, T4 cells are only weakly directional(Douglass and Strausfeld, 1996). The T5 cells must eitherreceive input from cells that are already post-synaptic to thefinal subtraction stage of the EMDs or their input synapseswould have to form the neural substrate for that final sub-traction. T4 cells may represent a non-opponent stage inthe motion detection mechanism.

Recordings from unidentified cells in the medullahave shown that there are directional and non-directionalmovement-sensitive elements (McCann and Dill, 1969;Mimura, 1971; DeVoe and Ockleford, 1976; DeVoe, 1980).DeVoe (1980)recorded from cells in the medulla that re-sponded to moving gratings with maintained non-directionaldepolarizations but often had directional oscillations orspikes superimposed on the depolarized signal. This typeof response may arise from a cell that forms part of thebuilding block of an elementary motion detector.DeVoe(1980) suggested that the characteristic response wave-forms could be explained by multiplicative inputs fromlamina and medulla cells to the movement detector units.However, no anatomical evidence was then available toconfirm this pathway. A major indicator for this idea wasthat motion in one direction in some medulla neurons pro-duced clear second harmonic response components whilemotion in the opposite direction produced either no secondharmonics or low-amplitude second harmonic components.As outlined inSection 2.4.1, the existence of second har-monics in the responses to moving gratings suggests asecond-order non-linear mechanism in the motion detector.This is an expectation predicted for the components of boththe Reichardt and Energy models (Egelhaaf et al., 1989;Emerson et al., 1992). Gilbert et al. (1991)suggest that thesecond-order non-linearity may occur between the outputsof L2 laminar neurons and medulla cells.

The extracellular electrophysiology certainly points to-wards the medulla or the interface between the lamina andmedulla as a likely source for motion detector interactions.What do we know of the anatomy of those areas of the opticlobe? The main inputs to T4 and T5 cells appear to arise fromthe transmedullary cells Tm1, iTm and, in certain species,from Tm1a, Tm1b and Tm9 (Fischbach and Dittrich, 1989;Douglass and Strausfeld, 1998). It is thought that the termi-nals of the iTm cells are presynaptic to the dendrites of T4cells and that the terminals of Tm1, Tm1a, Tm1b and Tm9cells are presynaptic to the dendrites of T5 cells (Fig. 14).Physiological evidence shows that some Tm cells are direc-tional but that the responses are not fully motion opponent(Gilbert et al., 1991; Douglass and Strausfeld, 1995). It isprobable that the Tm1 cells are one of the components thatmake up the elementary motion detectors proposed in theReichardt and/or Energy models.

It is of course essential that some type of lateral spatialinteraction occur in the motion detectors between neighbor-ing regions of the visual field. There are several possiblesources of lateral interactions. Firstly, it appears that thelateral interactions might occur at the transition from thelamina to the medulla. The lamina contains several typesof L-monopolar cells in each optic cartridge, the latter con-taining all the neural tissue that lies underneath each facetlens of the eye. L2 monopolar cells may provide inputsfrom adjacent optic cartridges to Tm1 neurons via lateralconnections involving T1 basket cells and lamina amacrinecells (Fig. 14). T1 cells terminate in the medulla betweenthe terminals of L2 monopolar cells and the dendrites ofTm1 cells and receive input from lamina amacrine cells,which receive their signals from several photoreceptors indifferent cartridges (Douglass and Strausfeld, 2001). Sec-ond, in the lamina, the L4 monopolar cells and the laminaamacrine cells (Fig. 14) provide a system of connections be-tween retinotopic columns (Strausfeld and Campos-Ortega,1973). In this case, the L4 neurons receive input from lam-ina amacrine cells and then distribute this information to L2and Tm1 neurons in different columns. Finally, the C2 neu-ron provides feedback from the inner layer of the medullaback to the outer medulla and lamina (Strausfeld, 1976).The C2 neurons cross between retinotopic columns, thusproviding another possible source for lateral interactions inthe motion processing pathway (Fig. 14).

Visually responsive neurons that send their axons fromthe midbrain area back, centrifugally, into the medulla havebeen identified in several insects (e.g. moths:Collett, 1970,1971; Milde, 1993; butterflies: Ibbotson et al., 1991). Inboth moths and butterflies the dendrites of the centrifugalneurons are in the midbrain area occupied by the outputs ofdirection-selective neurons from the lobula plate. The largecentrifugal neurons are highly direction-selective, so fullymotion-opponent signals from the midbrain are sent into thedistal layers of the medulla. This is of significance for anyphysiologist recording from small centrally directed neuronsin the medulla because any observed directionality may be


the result of signals fed back from the midbrain rather thansignals arising from elementary motion detectors.

In conclusion, the insect optic lobes provide a neural sub-strate that has the potential to reveal the exact structure of thelocal motion detector networks in a biological system. Farmore attention has been given to the large direction-selectiveoutput neurons of the optic lobes than to the complex neu-ral networks that probably form their input. It is certainlytechnically difficult to record from the small neurons in themedulla but the rewards of doing so could be substantial.It would be greatly beneficial to follow the course taken bythe pioneers that have attempted to record from the motiondetector pathways in the insect medulla (e.g.DeVoe andOckleford, 1976; DeVoe, 1980; Osorio, 1986; Douglass andStrausfeld, 1995, 1996, 2001). However, in trying to findthe elementary motion detectors in the outer optic lobes wemust be careful to take into account any feedback systemsthat provide fully motion opponent signals to the outer opticlobes from the midbrain, as found in butterflies (Ibbotsonet al., 1991).

4. Adaptive mechanisms in motion detection

4.1. Perceptual consequences of motion adaptation

Visual analysis of the world is an active process involv-ing the continual adaptation of elementary processing units.Rapid neural adaptation is a fundamental property of vi-sion with moment-to-moment relevance to our perception(Muller et al., 1999; Dragoi et al., 2002). Prolonged adap-tation to a moving stimulus has profound perceptual conse-quences. When fixation is transferred to a stationary pattern,illusory motion is seen in the direction opposite to the adapt-ing motion, but with little or no accompanying change inperceived position (Nishida and Johnston, 1999; Snowden,1998). This “motion aftereffect” (MAE) has been knownsince ancient Greece, and has been studied extensively overthe past 40 years (seeWade and Verstraten, 1998). Motionadaptation also affects the subsequent perception of movingstimuli, causing shifts in perceived direction (Levinson andSekuler, 1976; Patterson and Becker, 1996; Schrater andSimoncelli, 1998; Rauber and Treue, 1999; Alais and Blake,1999) and speed (Goldstein, 1957; Carlson, 1962; Rapoport,1964; Thompson, 1981; Smith and Hammond, 1985; Mullerand Greenlee, 1994; Clifford and Langley, 1996a; Bex et al.,1999; Clifford and Wenderoth, 1999; Hammett et al., 2000).In addition, the effect selectively impairs the ability to detectlow contrast (Levinson and Sekuler, 1980) or incoherentmotion (Raymond, 1993a,b; Hol and Treue, 2001).

An early account of the neural basis of perceptual after-effects was that adaptation satiates or fatigues cells sensi-tive to the adapting stimulus (Kohler and Wallach, 1944;Sutherland, 1961). When the period of adaptation ceases,the spontaneous discharge of the fatigued cells remains sup-pressed (Barlow and Hill, 1963). This produces a bias away

from the adapted stimulus in the response of the populationof cells sensitive to the adapted stimulus dimension, givingrise to a perceptual repulsion effect. So, for example, afteradaptation to a downwards-moving pattern, a static patternwill appear to drift upwards due to fatiguing of cells prefer-ring downwards motion (Wohlgemuth, 1911; Mather et al.,1998).

Motion adaptation impairs the ability to detect subsequentmotion in a direction-selective manner, such that motion co-herence thresholds are maximally elevated in the adaptingdirection (Raymond, 1993a; Hol and Treue, 2001). Thesethreshold elevations show complete inter-ocular transfer,demonstrating that they are of cortical origin (Raymond,1993b). It is generally assumed that motion detection isdetermined by the responsiveness of the neuron most sen-sitive to the test direction. As a result of adaptation, theresponsiveness of neurons tuned to the adapting direction isreduced most, with the reduction in responsiveness of anygiven neuron determined by the angle between the adapt-ing stimulus direction and that neuron’s preferred direction(Kohn et al., 2001).

4.2. Function or fatigue?

The importance of light adaptation by photoreceptor cellsin the retina is well established, enabling our visual systemsto operate in a vast range of conditions from near darknessto bright sunlight (Barlow, 1969; Laughlin, 1994). Adapta-tion at subsequent stages of the visual system is also welldocumented, but has often been viewed as a limitation of thesystem associated with neural fatigue (Kohler and Wallach,1944; Sutherland, 1961). However, physiological data fromarea 17 of cat show that adaptive contrast gain control mech-anisms operate at a cortical level (Ohzawa et al., 1982), andsuggest that transient temporal mechanisms might adapton the basis of stimulus motion or temporal modulation toimprove temporal frequency discrimination (Maddess et al.,1988). Physiological studies on the pattern-specificity of theneuronal response to motion adaptation (Hammond et al.,1989; Saul and Cynader, 1989) and demonstrations of thestorage of aftereffects (Wohlgemuth, 1911; Wiesenfelderand Blake, 1992) provide further evidence that there is moreto motion adaptation than neural fatigue.

If cortical adaptation cannot be attributed to neural fatigue,it is reasonable to ask whether it serves a function analogousto light adaptation in the retina. The retina codes varia-tions in luminance by adapting to, and hence discounting,the mean luminance. Light adaptation has clear functionalbenefits in ecological terms, allowing the visual system tooperate over a huge range of light levels. While informationabout the illuminant is discarded, this is of little relevancein comparison to the preservation of luminance changescarrying information about the structure of the environment.However, when one considers motion adaptation rather thanlight adaptation, the appropriateness of such a strategy is lessclear.


It has been argued that, in flying insects, it is moreimportant for motion-sensitive neurons involved in the sta-bilization of flight to optimize their sensitivity to changesin image motion rather than to provide an accurate measureof absolute speed (Shi and Horridge, 1991). The logicof this argument is that, to maintain stability in flight, itis more important to be able to detect small perturbationsin trajectory rather than to be continually reminded of thespeed of flight. It is important to realize that this argumentonly holds for the stabilization-system. For other behaviors,such as measuring the distance traveled during foraging,insects use information on the absolute speed of opticflow during forward flight (Esch et al., 2001; Srinivasanet al., 2000). It is probable that insects use both absolutespeed information and changes in speed to obtain their fullrepertoire of actions. Different types of cell specialized forforward flight detection have been identified, some show-ing phasic response properties, presumably for detectingchanges in speed (Ibbotson, 1991a, 1992), while othersmaintain a steady firing rate during stimulation (Ibbotson,1991b). Recordings from the neurons that continue tofire throughout a period of stimulation have shown thatthe mean level of the response decreases, correspondingto a drop in absolute sensitivity to motion (Ibbotson andGoodman, 1990; Ibbotson, 1992; Maddess and Laughlin,1985). At the same time, there is an increase in themagnitude of changes in response to variation in imagemotion around the adapting level (Fig. 15), correspond-ing to an improvement in differential motion sensitivity(Maddess and Laughlin, 1985; Maddess et al., 1991; Shi andHorridge, 1991).

In mammalian vision, one can think of situations wheresacrificing information about absolute motion for enhanceddifferential motion sensitivity would be advantageous; a bearfishing in a stream, for example, could use differences inthe speed of motion to detect the presence of its prey. Butthere are also situations, as with insects, where accurate es-timation of absolute motion appears important; e.g. in pre-dicting the trajectories of moving objects and the guidanceof pursuit eye movements (Priebe et al., 2001). Thus, whilethe benefits of enhanced sensitivity to changes in luminanceand changes in motion might appear analogous, the cost of

Fig. 15. Neuronal model of the effect of adaptation on absolute and differential sensitivity to the speed of image motion. (A) Adaptation produces arightward shift of the response function. (B) This rightwards shift causes the neuronal response to drop over time. (C) The rightward shift positionsasteeper part of the response function at the speed of the adapting stimulus, thus increasing the amount by which the response changes for a given changein speed.

discarding information about absolute motion seems muchhigher than the cost of losing information about the meanlight level. Intriguingly, a recent report byFairhall et al.(2001)suggests that rapid adaptation of the input/output re-lationship of the fly H1 neuron to different distributions ofstimulus motion need not necessarily mean that informationabout the adapting level be discarded. Instead,Fairhall et al.(2001)find that information about the statistics of the stim-ulus ensemble are encoded by the statistics of the interspikeinterval distribution on a timescale only slightly longer thanthat of rapid adaptation.

4.3. Informational basis of motion adaptation

From an information-processing standpoint, a possiblefunction of motion adaptation is to work towards the ro-bust and efficient transmission of signals coding for imagemotion. The constraints on neural information transmis-sion are that signals must be passed through channels oflimited bandwidth that are subject to transmission errors(Attneave, 1954; Barlow, 1961; Laughlin, 1989; Cliffordand Langley, 1996a). These limitations are analogous tothose faced in telecommunications applications where it isoften advantageous to code signals adaptively so that thebest compromise can be reached between maximizing effec-tive bandwidth and minimizing the effects of transmissionerrors. Consequently, functional ideas about adaptation havebeen motivated by two main considerations: self-calibrationand dynamic range optimization. Self-calibration is theproperty of a system to change itself in response to changesin the environment (recalibration) and to adjust to pertur-bations within the system in an unchanging environment(error-correction) (Andrews, 1964; Rushton, 1965; Ullmanand Schechtman, 1982). Dynamic range optimization tendsto reduce redundancy in the responses of individual sensoryneurons (Attneave, 1954; Barlow, 1961, 2001), maximiz-ing the effective bandwidth available for the transmissionof novel information about the stimulus (Srinivasan et al.,1982; Laughlin, 1989; Clifford and Langley, 1996a). Empir-ical support for these ideas comes from electrophysiologicalstudies of the fly H1 neuron which have shown formallythat adaptation to motion tends to maximize information


transmission (Brenner et al., 2000; Fairhall et al., 2001).The principle of redundancy reduction can be extendedfrom single neurons to populations of neurons by adaptivelydecorrelating (Barlow and Foldiak, 1989) or orthogonaliz-ing (Kohonen and Oja, 1976) their responses, and may beapplicable to the coding of motion in human visual cortex(Clifford et al., 2000; Clifford, 2002).

4.4. Dynamics of motion adaptation

Adaptation to motion has been shown to generate large,robust aftereffects with identified neural correlates in thecat (Hammond et al., 1988; Giaschi et al., 1993), monkey(Petersen et al., 1985; van Wezel and Britten, 2002) andhuman cortex (Tootell et al., 1995; He et al., 1998; Culhamet al., 1999; Huk et al., 2001). The principal neural sub-strate of the MAE in human visual cortex is believed tobe the human homologue of monkey area MT (Fig. 16;Tootell et al., 1995; He et al., 1998; Culham et al., 1999;Huk et al., 2001), in which the vast majority of neuronsare strongly direction-selective (Albright et al., 1984). Abehavioral analogue of the MAE has also been observedin the optomotor response of the blowfly (Srinivasan andDvorak, 1979). Neural correlates of motion adaptation sim-ilar to those observed in the mammalian cortex have beenobserved in direction-selective neurons in a range of in-sects (fly:Maddess and Laughlin, 1985; bee:Ibbotson andGoodman, 1990; butterfly:Maddess et al., 1991).

Fig. 16. The motion aftereffect and human area MT. (a) Stationary view of the stimulus used byTootell et al. (1995). (b) Human cortical visual area MT(V5) activated by that stimulus. The brain is shown in both normal and “inflated” format. Sulcal cortex (concave) is dark magenta and gyral cortex (convex)is lighter magenta. The functional magnetic resonance imaging (fMRI) activity produced by moving minus stationary rings is coded in a pseudocolor scalevarying from saturated magenta (threshold) to white (maximum activity). The prominent white patch on the bottom right lateral surface is area MT (V5).MT (V5) showed a clear increase in magnetic resonance signal amplitude during viewing of stationary stimuli when they were preceded by an adaptationstimulus moving continuously in a single direction. Reprinted with permission fromNature(Tootell et al., 1995). Macmillan Magazines Limited (© 1995).

Human psychophysical studies have shown that perceivedspeed is affected by prior adaptation to motion (Thompson,1981; Smith, 1987), and even to stationary stimuli (Held andWhite, 1959; Clifford and Wenderoth, 1999). When adapt-ing and test stimuli have the same contrast, speed and direc-tion, perceived speed is consistently decreased by adaptation(Carlson, 1962; Rapoport, 1964; Thompson, 1981; Mullerand Greenlee, 1994). Correspondingly, the perceived speedof a constantly moving stimulus decreases as a function ofadaptation duration (Goldstein, 1957), decaying exponen-tially to a steady-level (Clifford and Langley, 1996b; Bexet al., 1999; Hammett et al., 2000). As the perceived speed ofa constantly moving stimulus decreases, sensitivity to mod-ulations or increments in speed is enhanced (Clifford andLangley, 1996b; Bex et al., 1999; Clifford and Wenderoth,1999), at least for luminance-defined motion (Kristjansson,2001), suggesting that an accurate representation of absolutespeed is sacrificed for greater differential sensitivity. Speedincrement thresholds remain approximately proportional toperceived speed during adaptation and recovery from adap-tation (Bex et al., 1999; Clifford and Wenderoth, 1999) sothat, as perceived speed decreases through exposure, theability to detect small changes around that speed improves.

The psychophysics of human motion adaptation paral-lels closely electrophysiological data recorded from thedirection-selective H1 neuron in the lobula plate of the fly(seeSection 3.4) in both form and time course (Cliffordand Langley, 1996b). In the fly, prolonged exposure to


maintained motion causes the response of H1 to decay ex-ponentially over time to a steady level. The time constantof the response decay is of the order of 2–3 s for stimulimoving at around 50◦ per second (Maddess and Laughlin,1985). In human vision, reported time constants range from1 to 16 s dependent upon stimulus parameters (Clifford andLangley, 1996b; Bex et al., 1999; Hammett et al., 2000).

4.5. Directionality of motion adaptation

Clifford and Wenderoth (1999)found that adaptation tomotion per se is not required to enhance differential speedsensitivity in humans. Adaptation to temporal modulationin the absence of net motion was found to produce signifi-cant improvements in discrimination around the subjectivematching speed. Discrimination thresholds were found todecrease in proportion to perceived speed, regardless of thedirection of motion or orientation of the flickering grating.Thus, it appears that, in human vision, enhancements in dif-ferential speed sensitivity are driven largely by adaptationto temporal modulation rather than to motion itself.

Curiously, the finding that motion adaptation is driven bytemporal modulation rather than motion per se is analogousto electrophysiological data from the H1 and HSE neuronsof the fly lobula plate (Borst and Egelhaaf, 1987) but dis-tinct from those for motion-sensitive neurons in wallabyNOT (Clifford et al., 1997; Ibbotson et al., 1998). Borst andEgelhaaf (1987)measured the time constant of the decayof the response to impulsive (two-frame) motion in fourconditions: control (no adaptation); preferred motion adap-tation; anti-preferred motion adaptation; and adaptation tocounter-phase flicker. In all except the control conditionthey found that the decay time constant reduced to between20 and 40% of its unadapted value, suggesting that adapta-tion in that system, as for human speed perception, is drivenby temporal modulation (Fig. 17).

In the NOT of the mammalian wallaby, preferred di-rection motion causes the most significant adaptation.Anti-preferred motion or flicker induces some adaptation,but it is far weaker than that induced by preferred direc-tion motion (Clifford et al., 1997; Ibbotson et al., 1998).However, the use of the time constant of the decay of theresponse to impulsive (two-frame) motion as an index ofadaptation has recently been criticized on the basis thatchanges in the time constant as a function of adaptationcan be modeled by the introduction of fixed high-pass tem-poral pre-filters prior to motion computation (Harris andO’Carroll, 2002). Thus, a caveat must be applied to theuse of impulse response data to infer the determinants ofmotion adaptation as in the wallaby experiments. Indeed,Harris et al. (2000)have shown that adaptation to motion inthe HS neuron of the fly lobula plate is not simply driven bythe associated temporal modulation of contrast. This workis discussed in detail inSection 4.6.

In humans, prolonged exposure to a moving pattern af-fects not only the perceived speed but also the perceived

Fig. 17. Motion-dependence and direction-selectivity of adaptation. Nor-malized decay time constants of the impulse responses of the insect H1neuron (black) and DS neurons in the NOT (gray) compared with humanpsychophysical perceived speed data (hashed). The response of the insectH1 neuron is adapted to a similar degree by preferred direction motion,anti-preferred motion and flicker (Borst and Egelhaaf, 1987). The humanperceived speed data follows a similar pattern (Clifford and Wenderoth,1999). In contrast, the response of the NOT neurons is much more stronglyadapted by preferred direction motion than by anti-preferred motion orflicker (Clifford et al., 1997; Ibbotson et al., 1998).

direction of subsequent motion (Levinson and Sekuler,1976; Patterson and Becker, 1996; Schrater and Simoncelli,1998; Rauber and Treue, 1999; Alais and Blake, 1999). Thisphenomenon, known as the direction aftereffect (DAE), dif-fers from the classical MAE in that a moving test stimulus isused to measure the DAE. For angles up to around 100◦ be-tween the directions of motion of the adapting and test pat-terns, the perceived direction of the test pattern tends to berepelled away from the adapting direction. The magnitudeof this repulsion can be as much as 40◦ for adapter-test an-gles around 30◦. For larger obtuse angles between adaptingand test directions, the perceived direction of the test tendsto be attracted towards that of the adapter (Schrater andSimoncelli, 1998). The magnitude of this attraction effectis smaller than that of the repulsion, peaking at around 15◦for angles of around 150–160◦ between adapter and testdirections.

The effect of motion adaptation on subsequent directiondiscrimination depends upon the angle between the adaptingdirection of motion and the baseline test direction aroundwhich discriminations are made. For parallel adapting andtest directions,Phinney et al. (1997)found reductions in di-rection discrimination thresholds of around 20% whileHoland Treue (2001)found little or no effect of adaptation. Thisdiscrepancy in the magnitude of the effect of adaptation onsubsequent discrimination around the adapting direction re-mains mysterious. However, it is interesting to note thatHoland Treue (2001)also report smaller effects of adaptation onmotion detection than didRaymond (1993a), suggesting thattheir adaptation paradigm might somehow be less powerful


than those used in other studies. For angular differences of10–40◦ between adapting and baseline test directions, adap-tation impairs subsequent discrimination performance, withmaximum threshold elevations of around 60% at adapt-testangular differences of 20–30◦ (Phinney et al., 1997; Hol andTreue, 2001).

Thus, for the direction as well as the speed of motion,there is psychophysical evidence that adaptation improvesdifferential sensitivity to motion around the adapting levelat the expense of the introduction of perceptual biases. Inthe case of speed, the perception of the actual adaptingstimulus is affected, while it is only the perception of di-rections of motion other than the adapting direction that isperturbed.

4.6. Distinguishing motion adaptation from contrastadaptation

The response of motion-sensitive cells in the primaryvisual cortex (V1) is modulated not only by stimulus tem-poral frequency but also by contrast (Sclar et al., 1990).Indeed, it seems likely that temporal frequency adaptationand contrast adaptation share V1 as a neural substrate. Nu-merous studies have shown that speed and contrast are notindependently coded (e.g.Muller and Greenlee, 1998), andthat adaptation decreases perceived contrast (Blakemoreet al., 1973; Georgeson, 1985; Hammett et al., 1994). Stim-ulus contrast has in turn been shown to affect perceivedspeed over a wide range of contrasts (Thompson, 1982;

Fig. 18. The effects of adaptation on the contrast response function of the insect HS neuron (Harris et al., 2000). (A) Schematic of characteristic sigmoidalneuronal response as a function of log contrast. Adaptation induces: (B) an after-potential that shifts the contrast response function vertically;(C) contrastgain reduction that shifts the curve to the right; and (D) output range reduction that shifts the function to the right and reduces its maximum.

Stone and Thompson, 1992; Muller and Greenlee, 1994;Thompson et al., 1996) and, at low contrasts, speed dis-crimination thresholds (Muller and Greenlee, 1994). Howcan we be sure that the effect of motion adaptation can-not be accounted for in terms of the fading of perceivedcontrast accompanying adaptation? Firstly, perceived speedhas been shown to decrease with grating adaptation evenwhen contrast fading has been controlled for, such that testand reference gratings have perceptually matched contrasts(Clifford, 1997; Bex et al., 1999), and to decrease at a slowerrate than perceived contrast (Clifford, 1997; Hammett et al.,1994). Secondly, while stimulus contrast has been found toaffect speed discrimination (Muller and Greenlee, 1994),thresholds reach a lower asymptote by 10% contrast. Inthe range where contrast does affect speed discrimination,thresholds increase with decreasing contrast. Thus, whileresponse gain control at the level of V1 might underlieadaptation both to contrast and to temporal modulation, theeffects of adaptation on perceived speed are not predictablesimply on the basis of contrast fading.

In the wide-field motion-sensitive HS neuron of thefly lobula plate,Harris et al. (1999)have described threechanges in the contrast response function induced by mo-tion adaptation. These are an after potential, a contrast gainreduction and a reduction in the cell’s output range. Plot-ting neuronal response as a function of the logarithm ofcontrast, these changes have the effect of shifting the re-sponse function down, shifting it to the right and reducingits maximum, respectively (Fig. 18).


The after potential appears to be dependent upon theactivity of the cell during adaptation (Harris et al., 1999)and is probably associated with dendritic calcium accumu-lation (Kurtz et al., 2000). The greater the depolarizationinduced during adaptation, the greater the hyperpolarizingafter potential and the greater the downward shift of thecontrast response function. Adaptation to flicker or motionorthogonal to the preferred direction induces only a weakhyperpolarizing after potential. Anti-preferred motion hy-perpolarizes the cell during adaptation, inducing a weakdepolarizing after potential that shifts the contrast responsefunction slightly upwards. Thus, the vertical shift of thecontrast response function through adaptation shows a highdegree of direction-selectivity.

While the after potential is highly direction-selective,adapting motion in any direction reduces contrast gain by asimilar amount. Flicker induces a much smaller reductionin gain, suggesting that contrast gain reduction is dependenton a mechanism that is both motion-dependent and direc-tion insensitive. The fact that flicker reduces contrast gainonly weakly suggests that contrast gain control is driven bya signal obtained after the motion opponent stage that servesto attenuate flicker responses. To account for the directioninsensitivity of contrast gain control, this signal would haveto involve the pooled responses of motion detectors tuned todifferent directions. However, the reduction in contrast gainappears to be retinotopic, such that the rightward shift in thecontrast response function of the wide-field HS cell is onlyobserved when adapting and test stimuli are presented in thesame location. This retinotopy suggests that gain reductionmust be occurring either in retinotopic elements presynapticto HS or locally on its dendrites. Until more is known aboutthe anatomy and physiology of the visual pathway afferentto the lobula plate (seeSection 3.4), the mechanisms ofcontrast gain control are likely to remain unclear.

A marked reduction in output range is only observed afteradaptation to preferred or anti-preferred motion, not afteradaptation to orthogonal motion or flicker. This suggeststhat range reduction is activity dependent, being induced byadapting stimuli that cause either a sustained depolarizationor a sustained hyperpolarization of the cell. Thus, all threecomponents of adaptation observed byHarris et al. (1999)in the response of the fly HS neuron are driven stronglyby preferred direction motion but only weakly if at all byflicker, demonstrating that they are the result of adaptation tomotion per se rather than the associated temporal modulationof contrast.

5. Concluding remarks

While evolution appears to have discovered multiplesolutions to the problem of motion detection, there is aremarkable similarity between motion processing mecha-nisms in species as diverse as insects and humans. Furtherprogress in understanding the fundamental mechanisms of

motion detection will undoubtedly benefit from a continuedcomparative approach.

In this review, we have emphasized the value of linkingtheoretical and experimental approaches to motion detec-tion. We have described the basic structures of a range oftheoretical motion detectors that have been designed sinceExner (1894)sketched the first model. In recent years therehas been a flurry of “new” models in the literature but wehave tried to categorize them into a small number of mech-anisms that use closely related concepts. We have then re-viewed the evidence that these mechanisms are in operationat the cellular level in biological systems. “Model-chasing”has proved very popular with physiologists, pharmacologistsand anatomists in a range of species ranging from insectsto primates. Some of the motion detector models appear tobe implemented, at least in part, in the visual systems of arange of animal species. However, evolution appears to havesolved several issues in unique ways that have only becomeevident by opening the cockpit of the relevant species.

This review also emphasizes the importance of adapta-tion in the motion processing system. Psychophysicists havestudied motion aftereffects, which are generated by adap-tive mechanisms, for many years and yet it is only recentlythat science has really attempted to explain the phenomenon.Given that motion detection is a dynamic process, we feelthat it is important to consider motion adaptation as an in-tegral part of the motion detector process rather than as aseparate mechanism. With this in mind, the review has at-tempted to link adaptation to the cellular and theoreticalmechanisms of motion detection. We anticipate that treatingadaptation as a fundamental property of a complex systemof filters will continue to advance our understanding of thedynamic aspects of vision in general and of motion detec-tion in particular.

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