Progress in Neurobiology 68 (2003) 409–437

Fundamental mechanisms of visual motion detection: models, cells and functions C.W.G. Clifford a,∗, M.R. Ibbotson b,1 a Colour, Form and Motion Laboratory, Visual Perception Unit, School of Psychology, The University of Sydney, Sydney 2006, NSW, Australia b 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 motion detection. The cellular basis of motion detection in the vertebrate retina, sub-cortical structures and visual cortex is reviewed alongside that 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 of non-linear mechanism such as facilitation, inhibition and division have been proposed to underlie direction selectivity. The resulting direction-selective mechanisms can be combined to produce speed-tuned motion detectors. Motion detection is a dynamic process with adaptation as a fundamental property. The behavior of adaptive mechanisms in motion detection is discussed, focusing on the informational basis of motion adaptation, its phenomenology in human vision, and its cellular basis. The question of whether motion adaptation serves a 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 ...... 410 2. General motion detector mechanisms ...... 410 2.1. Fundamentals of motion detection ...... 410 2.2. Pre-filtering ...... 410 2.2.1. On- and Off-channels ...... 410 2.2.2. Temporal characteristics of pre-filters ...... 412 2.3. Temporal delay filtering...... 413 2.4. Non-linear interactions...... 415 2.4.1. Facilitation ...... 416 2.4.2. Inhibition ...... 417 2.4.3. Speed-tuned motion detectors ...... 418 3. Evidence for the cellular mechanisms of motion detection...... 419 3.1. Retinal motion detectors in vertebrates ...... 419 3.2. Sub-cortical motion processing ...... 421 3.3. Cortical motion processing ...... 422 3.4. 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, dorsal terminal 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 terminal nucleus; NOT, nucleus of the optic tract; PMLS, posteromedial lateral supersylvian area; RGC, retinal ganglion cell; STOLF, space–time oriented linear filter; 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 ...... 426 4.1. Perceptual consequences of motion adaptation ...... 426 4.2. Function or fatigue? ...... 426 4.3. Informational basis of motion adaptation ...... 427 4.4. Dynamics of motion adaptation...... 428 4.5. Directionality of motion adaptation ...... 429 4.6. Distinguishing motion adaptation from contrast adaptation ...... 430 5. Concluding remarks...... 431 References ...... 431

1. Introduction 2. General motion detector mechanisms

While numerous visual animals lack color or binocular 2.1. Fundamentals of motion detection vision, the ability to see motion is ubiquitous and, next to the detection of light and dark, may be the oldest and Exner (1894) was the first person to discuss the require- most basic of visual capabilities (Nakayama, 1985). Conse- ments necessary for generating a motion signal from neural quently, visual motion processing is of fundamental interest circuitry. He presented a drawing of a neural network that to systems neuroscience and has been the subject of in- can be regarded as the first attempt at a motion detector tense research. The present paper reviews recent advances model (Fig. 1A). However, it was another German scientist, in our understanding of motion detection in biological Reichardt (1961), who promoted the first computation- systems in the context of the large body of work that has ally based model of motion detection (Hassenstein and gone before. In particular, we emphasize the contribution Reichardt, 1956), a model that has subsequently been given made by comparative studies of motion detection to the his name (Fig. 1B). Although motion detector models vary broader understanding of the topic. The modern theoretical in their detailed structure, the Reichardt detector is useful framework for motion detection was developed from behav- in setting out the basic framework necessary for motion ioral experiments on the Chlorophanus beetle (Hassenstein detection (e.g. Borst and Egelhaaf, 1989). and Reichardt, 1956; Reichardt, 1961). The relevance of Detecting the direction of motion requires that the im- this early work to subsequent studies of motion detection, age be sampled at more than one position or spatial phase, including primate cortical physiology and human psy- that these samples be processed asymmetrically in time, and chophysics, indicates the importance of a broad biological that they be combined in a non-linear fashion (Poggio and approach. A key feature of motion processing to emerge at Reichardt, 1973; Borst and Egelhaaf, 1989). This is a se- both the cellular and systems levels is its dynamic nature rial process that involves computation at multiple synaptic and adaptive plasticity. Consequently, motion adaptation levels. These stages will be covered in three sub-sections: will be a major focus of this review. Perhaps the next great pre-filtering, delay filtering and non-linear interactions. challenge in understanding motion detection is to reconcile the wide range of theoretical approaches with the cellu- 2.2. Pre-filtering lar basis. Given the important advances that have already been made in this direction, we review progress on both of While Reichardt’s (1961) original model included spatial these levels. and temporal pre-filters (Fig. 1B), many subsequent mod- The moving world is projected onto the retina in the form els of motion detection have neglected the importance of of a spatiotemporal pattern of light intensity. From this dy- pre-filtering (although see van Santen and Sperling, 1984, namic signal, recovery of the direction of image motion is 1985; Ibbotson and Clifford, 2001a,b). Pre-filters are impor- the first stage of extracting behaviorally relevant informa- tant because their properties affect the tuning characteristics tion. Section 2 of this review will cover motion processing of the motion detectors they feed. What do we know about from initial non-directional filtering strategies up to the the pre-filters of biological motion detectors? point where a directional signal is produced. Section 3 then deals with the evidence for cellular mechanisms that might 2.2.1. On- and Off-channels perform these tasks in a range of brain areas and species. It is well established that vertebrate photoreceptors are Specifically, we look at motion processing in the vertebrate hyperpolarized by light and their outputs are fed into bipo- and insect visual systems. Section 4 deals with the im- lar cells. Sign conserving synapses feed Off-bipolar cells portant role performed by adaptive mechanisms in motion while sign inverting synapses feed On-bipolar cells (Werblin processing. and 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 by Exner (1894). Retinal fibers feed into points a–f and similar points. Signals from these points are summed at sites S, E, Jf and Jt. The time taken for a signal from 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 different retinal locations that introduces directionality into the scheme. (B) Schematic of the mathematical model proposed by Reichardt (1961) to describe the optomotor response to motion stimuli in the Chlorophanus beetle. Ommatidia A and B are separated by an angular distance, s. The temporal responses, LA and LB, from these receptors are linearly transformed by the units D, F and H and linked together in the multiplier units, MA and MB. The outputs of the multiplier units are passed through low-pass temporal filters, SA and SB, and then subtracted from each other. The output of the subtraction stage controls the motor response of the beetle.

by brightness increments (On stimuli) while Off-cells are How do signals from the On- and Off-channels interact excited only by brightness decrements (Off stimuli). Just in the generation of direction-selective responses? A classic as the On- and Off-cells are excited by opposite bright- example of the way that pre-filtering affects the outputs of ness polarities, they are also inhibited in a polarity depen- directional neurons comes from rabbit retina, where two dent fashion, so On-cells are inhibited by Off stimulation distinct types of direction selective (DS) retinal ganglion and Off-cells by On stimulation (e.g. Enroth-Cugell and cells have been identified: On-DS cells and On–Off-DS cells Robson, 1966; Hochstein and Shapley, 1976). Since On- (Barlow et al., 1964; Barlow and Levick, 1965). Both cell and Off-cells are inhibited by brightness decrements and in- types generate direction-selective responses. On-DS cells crements, respectively, why are both On- and Off-channels respond to the movement of bright bars while On–Off-cells necessary when a single channel might suffice to carry infor- respond to the movement of both bright and dark bars. mation about both brightness polarities? Schiller et al. (1986) Systems in other species show qualitatively different in- suggest that coding information about both increments and teractions between On- and Off-channels. For example, DS decrements through opposing excitatory processes is much cells in the pretectal nucleus of the optic tract (NOT) of the more efficient than using excitation and inhibition within marsupial wallaby (Ibbotson and Clifford, 2001a), the pri- a single channel. They reason that the low spontaneous mary visual cortex of the cat (Emerson et al., 1987, 1992) activity of retinal ganglion cells (RGCs) means that in- and macaque middle temporal area (Livingstone et al., hibition below this level cannot represent much informa- 2001) show interactions between On- and Off-signals that tion, while a higher spontaneous rate would have a high utilize the sign of the incoming signals. In the insect visual metabolic cost (Laughlin et al., 1998). The coding of bright- system, the retinal image is not segregated into On- and ness through On- and Off-channels might thus be the most Off-channels and wide-field direction-selective neurons in efficient way to satisfy both informational and metabolic the fly optic lobe receive input from motion detectors whose constraints. pre-filters maintain brightness polarity (Egelhaaf and Borst, 412 C.W.G. Clifford, M.R. Ibbotson / Progress in Neurobiology 68 (2003) 409–437

On- and Off-pathways converge at this level (Schiller, 1992). Behaviorally, the detection of light increments but not light decrements was severely impaired after injection of APB into the vitreous of the monkey (Schiller et al., 1986). The responses of DS cells in the middle temporal area (Zeki, 1974) and the medial superior temporal area (MST) of monkeys (Tanaka and Saito, 1989) have also been shown to be independent of contrast polarity, consistent with the notion that the On- and Off-pathways have already converged by this stage (see Section 2.3). For humans, Edwards and Badcock (1994) showed that psychophysical performance on a task requiring the global integration of local motion signals was similarly indepen- dent of contrast polarity. First, they found that the detection Fig. 2. Stimulus sequences and space–time plots of “phi” and “reverse-phi” of a global motion signal defined by a set of luminance motion. (Top left) Frames of an image sequence taken at times T1–T5 increment (On) dots moving in a common direction was show a white bar moving across a gray background at constant speed. Seen impaired equally by the addition of randomly moving in succession, the image sequence gives rise to the perception of “phi” “noise” dots of either contrast polarity. Second, they found apparent motion (Wertheimer, 1912), the bar appearing to move across the background. The sequence of frames may be placed together to form an sub-threshold summation for global motion signals carried image volume (top right), with time as the third dimension. (Bottom left) by a mixture of luminance increment (On) and luminance A slice through this space–time volume illustrates the fact that motion is decrement dots (Off). Thus, it seems that the inputs to equivalent to space–time orientation. (Middle row) A sequence of images individual motion detectors in the human visual system in which the contrast polarity of the bar reverses between black and white preserve contrast-polarity, as evidenced by the reverse-phi each time it moves. This image sequence gives rise to the percept of reverse-phi motion in the direction opposite to the displacement of the phenomenon (Anstis, 1970; Anstis and Rogers, 1975), but bar (Anstis, 1970; Anstis and Rogers, 1975). (Bottom right) Space–time that local motion information from these detectors is inte- plot of the reverse-phi motion stimulus. grated in a manner independent of the contrast-polarity of the original image signals (Edwards and Badcock, 1994).

1992). DS cells with inputs whose sign preserves brightness 2.2.2. Temporal characteristics of pre-filters polarity show characteristic responses to apparent motion If a motion detector received its inputs directly from stimuli consisting of increases or decreases in brightness. photoreceptors without any temporal filtering (Fig. 3), the Sequences of brightness steps of like polarity (either in- motion detector output would be modulated by motion crements or decrements) elicit positive motion-dependent but it would also respond strongly to a stationary image. response components to motion in the preferred direction Temporally band-pass filtering the image removes ongoing and negative responses to motion in the anti-preferred direc- brightness signals so that only changes in contrast enter tion. For sequences of opposite polarities, these directional the detectors (Srinivasan et al., 1982). Temporal band-pass properties are reversed (Fig. 2). These response properties filtering can be implemented in a biological system by neu- are reminiscent of the reverse-phi phenomenon in human rons with responses that are phasic. The impulse responses vision (Anstis, 1970; Anstis and Rogers, 1975). For a wide of such neurons, defined as the response to a brief flash, range of spatial and temporal displacements, humans per- consist of an initial excitatory phase followed by an in- ceive sequential brightness changes at neighboring positions hibitory period. The delayed temporal inhibition suppresses in the visual field as motion in the direction of the second the sustained response that would otherwise be generated brightness change (“phi motion”: Exner, 1875). When the by a steady light. Neurons of this type respond primarily sequential brightness changes are of opposite contrast po- to changes in light intensity while constant intensity light larity, motion in the reverse direction is perceived (“reverse produces virtually no response. Motion detectors receiving phi motion”: Anstis, 1970; Anstis and Rogers, 1975). input from such band-pass filters will be tuned to respond How are the On–Off interactions evident from psy- selectively to temporal variations in the image rather than chophysics implemented in the primate visual system? Using its unchanging components. This selectivity comes entirely an On-channel blocking agent, 2-amino-4-phosphonobutyric through pre-filtering strategies. It is also possible to reduce acid (APB), Schiller (1984) showed the On-response in the the response of the subsequent motion processing mecha- surround of lateral geniculate nucleus (LGN) Off-cells in the nism to unchanging image components through subtraction Rhesus monkey is not affected by APB. This demonstrates of the outputs of motion detectors tuned to opposite direc- that the On- and Off-channels remain independent up to and tions of motion (see Fig. 1B). including the level of the LGN. However, in cortical com- Ibbotson and Clifford (2001b) found evidence that the plex cells APB blocked the responses to moving dark/light pre-filters to the motion detectors feeding the mammalian (Off) but not light/dark (On) edges, suggesting that the pretectal nucleus of the optic tract adaptively match their C.W.G. Clifford, M.R. Ibbotson / Progress in Neurobiology 68 (2003) 409–437 413

Fig. 3. Impulse response functions of (A and C) low-pass and (E and G) band-pass temporal filters and their corresponding temporal frequency response functions. 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 to modulating a third-order envelope with a sinusoid. (B and D) The filters with monophasic temporal impulse responses have low-pass temporal frequency response functions. Cascading serves to narrow the pass-band (compare D with B). (F and H) The filters with temporally modulating impulse response functions have band-pass temporal frequency response functions.

response properties to the prevailing visual environment. In imizing signal strength at low contrasts (Srinivasan et al., that study, the response to two-frame apparent motion was 1982). Contrary to recent accounts (Strout et al., 1994; measured at a range of inter-stimulus intervals (ISIs) for Johnston and Clifford, 1995a), these modeling results sug- stimulus contrasts of 20 and 80%. The stimulus consisted of gest that the dependence of perceived direction of apparent two brief (10 ms) presentations of a sinusoidal grating sep- motion on ISI in human vision might reflect the implemen- arated by a variable ISI. Apparent motion was produced by tation of a general purpose image coding strategy in early displacing the second grating by one-fourth of a cycle rela- vision rather than a property particular to motion processing. tive to the first. For preferred-direction motion at the lower contrast, the response to the second frame of the apparent 2.3. Temporal delay filtering motion sequence was at least as large as the response to the first for all ISIs (Fig. 4). At the higher contrast, however, A temporal asymmetry is a necessary component of any the response to the second frame was facilitated for short direction-selective motion detector (Borst and Egelhaaf, ISIs (10–50 ms) but attenuated for longer ISIs (50–700 ms). 1989). This could come in the form of a temporal delay For ISIs between 50 and 700 ms, the response to the second filter (Reichardt, 1961) or in the form of a phase differ- frame was actually facilitated by anti-preferred motion. This ence between two temporally modulated filters (Adelson dependence of response sign on ISI duration is reminiscent and Bergen, 1985). Several attempts have been made to of a range of apparent motion phenomena in human vision in characterize the delay filters in biological motion detectors. which perceived direction of motion reverses for ISIs longer Srinivasan (1983) used a stimulus in which a textured pattern than around 60 ms (Shioiri and Cavanagh, 1990; Georgeson was displaced a small distance in a single 5 ms frame. This and Harris, 1990; Pantle and Turano, 1992). Ibbotson and impulsive image displacement was used to stimulate DS Clifford (2001b) found that this behavior can be modeled neurons in the insect optic lobe. The stimulus displacement by pooling the response of an array of elementary motion produced an initial rapid increase in firing rate followed detectors whose inputs preserve signal polarity and whose by an exponential decline in response level over the next pre-filter characteristics depend on stimulus contrast such 3 s. The response waveform was referred to as an impulse that pre-filtering is temporally low-pass at low image con- response (not to be confused with the common name for an trasts and band-pass at high contrasts (Fig. 4). action potential). Srinivasan (1983) was able to predict the Such a coding strategy would tend to reduce the transmis- response to continuous motion of the stimulus by convolut- sion of redundant information at high contrasts while max- ing the impulse response with the temporal profile of the 414 C.W.G. Clifford, M.R. Ibbotson / Progress in Neurobiology 68 (2003) 409–437

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 component is responsive to lower contrasts. Simulated response of an array of correlation detectors with these pre-filters as a function of inter-stimulus interval duration at stimulus contrasts of: (C) 80%; (D) 20%. Preferred, anti-preferred and non-motion conditions are represented by solid, dashed and dotted lines, respectively.

image motion. Several subsequent studies have used similar studies have failed to show a corresponding adaptive shift stimuli to record impulse responses of direction-selective in the preferred temporal frequency of the motion detectors neurons in insect and mammalian preparations (insects: de (Ibbotson et al., 1998; Harris et al., 1999), which should Ruyter van Steveninck et al., 1986; Maddess and Laughlin, remain inversely proportional to the length of the delay 1985; Borst and Egelhaaf, 1987; mammals: Ibbotson and (Borst and Bahde, 1986; Egelhaaf and Borst, 1989; Clifford Mark, 1996). and Langley, 1996a; Clifford et al., 1997), as illustrated Although impulse responses proved useful in predicting in Fig. 5. These later studies suggest adaptation at the the shape of response waveforms to continuous motion pre-filter level as a more likely substrate of the variation stimulation under certain stimulus conditions, they failed to in impulse response decay time constant as proposed by predict the temporal frequency response functions (TFRFs) Maddess (1986), although Harris and O’Carroll (2002) have of the neurons (Harris et al., 1999). The TFRF characterizes recently shown that variation in the impulse response decay the relationship between neuronal response magnitude and time constant can be modeled using fixed (non-adaptive) the rate of temporal modulation in the moving stimulus. The high-pass temporal pre-filters (see Section 4.5). results of Harris et al. (1999) and those of previous studies Harris et al. (1999) attempted to characterize the im- (Zaagman et al., 1983; de Ruyter van Steveninck et al., pulse response of the motion detector delay filters feeding 1986; Maddess, 1986; Borst and Egelhaaf, 1987; Ibbotson wide-field DS neurons in the insect optic lobes using an ap- and Mark, 1996) demonstrate that the time course of decay parent motion stimulus. The stimulus consisted of two brief of the impulse response is strongly dependent on stimulus presentations of a sinusoidal grating in which the second history. It has been argued that this dependence might re- presentation of the grating was displaced by one-fourth of flect adaptation at the level of the motion detector delay a cycle in the preferred direction of the cell. Harris et al. filter (de Ruyter van Steveninck et al., 1986; Clifford and (1999) plotted the magnitude of the response to the second Langley, 1996a; Clifford et al., 1997). However, subsequent flash as a function of the ISI between flashes. The resultant C.W.G. Clifford, M.R. Ibbotson / Progress in Neurobiology 68 (2003) 409–437 415

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 response denoted by the solid line has the longer time constant. (B) Temporal frequency response functions of the same filters. The filter with the shorter time constant (dotted lines) has the temporal frequency response function that peaks at the higher temporal frequency. For time constants in the ratio 2:1, the peak temporal frequencies are in the ratio 1:2.

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

2.4. Non-linear interactions

A linear combination of adjacent samples of the image can produce a difference in response modulation for the two directions of motion (Fig. 6; Jagadeesh et al., 1993, 1997) but will not produce a directional time-averaged Fig. 6. (Top) Space–time plot of a sinusoidal grating stimulus drifting at constant velocity. (Middle) Variation about the mean level (dotted line) response (Watson and Ahumada, 1985). To produce of the image signal at positions x1 and x2. The two signals have the direction-selective time-averaged outputs, a non-linearity same mean level, amplitude and temporal frequency but differ in temporal is required. As a consistent directional time-averaged out- phase. (Bottom) Delaying one signal relative to the other shifts their put is essential to signal the direction of motion, models relative temporal phase. For motion in the preferred direction, this results that include some form of non-linearity are used to model in constructive interference between the two temporal signals and a large variation about the mean level. Motion in the anti-preferred direction puts the responses of direction-selective neurons. The nature the two signals close to opposite temporal phases such that, in the sum, of the non-linear stage has received much attention from the variation about the mean level is small. Linear motion detectors of theoreticians and several models have been proposed (e.g. this kind are directional in their degree of response modulation but not Barlow and Levick, 1965; Adelson and Bergen, 1985; van in their mean response level. 416 C.W.G. Clifford, M.R. Ibbotson / Progress in Neurobiology 68 (2003) 409–437

Santen and Sperling, 1985; Egelhaaf et al., 1989; Amthor and Grzywacz, 1993; Johnston et al., 1992). We will now describe the types of non-linear interactions that have been considered and the evidence for their existence in biological systems.

2.4.1. Facilitation Conceptually, the simplest possible non-linear process is a direct facilitatory interaction such as a multiplication (Fig. 1B). This type of interaction was first proposed by Hassenstein and Reichardt (1956) in their Correlation model, subsequently referred to as the Reichardt detector, to account for behavioral data from the Chlorophanus beetle. While there is no evidence to suggest that multiplication occurs at a single 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- Fig. 7. Space–time oriented linear filters (STOLFs). Space–time plots eration such as squaring (Adelson and Bergen, 1985), recti- of STOLFs preferring motion (A) to the right, (B) to the left. The fication (Mizunami, 1990), or thresholding (Jagadeesh et al., preferred speed and direction of each STOLF corresponds to its orientation in space–time. (C) Rightwards motion of a white bar across the gray 1997). For example, Adelson and Bergen (1985) proposed background produces a space–time trajectory matched to the space–time an Energy model where signals from adjacent locations are receptive field structure of the STOLF preferring rightwards motion. (D) summed or subtracted. Such operations produce space–time Rightwards motion of a contrast-reversing bar produces a space–time oriented linear filters (STOLFs), i.e. filters in which re- trajectory better matched to the STOLF preferring leftwards motion, sponse latency varies systematically with spatial position. consistent with the perception of reverse motion. Using a prime to denote a delayed signal, the combina- tions of inputs from adjacent locations, A and B, that pro- duce space–time oriented linear filters are: A − B, A + B, ments on neurons in the mammalian nucleus of the optic B+A and B−A. As the combination is linear, a space–time tract also revealed mainly fundamental and second harmonic oriented linear filter will produce a response if A or B is responses (Ibbotson et al., 1994), suggesting the operation stimulated alone, with the latency of response depending on of a quadratic non-linearity in the motion detectors feeding the spatial position. Temporal coincidence is not required into the NOT. To test this suggestion Ibbotson et al. (1999); to generate a response from such filters. Conversely, mul- Ibbotson (2000) measured the responses of neurons in the tiplication will only give a response if both input channels NOT using apparent motion stimuli consisting of successive are stimulated with an appropriate time interval. Thus, while presentations of identical contrast changes in two adjacent direct (multiplication) and indirect (linear combination fol- bars. Increasing the contrast of the bars increased response lowed by squaring) mechanisms detect oriented structure magnitudes in an approximately quadratic fashion up to in space–time, only the indirect models contain space–time contrasts of 25%, supporting the notion of a facilitatory oriented linear filters (Fig. 7). The responses of such filters non-linearity (Fig. 8). However, values beyond that con- depend both on the direction-of-motion and the phase of the trast led to a saturating response function, such that the image signal. The Energy model combines the squared out- overall contrast response function had a sigmoidal appear- puts of these filters to produce directional responses, referred ance. These data emphasize that it is important to consider to as motion energy. The response of the Energy model at the effect of other non-linearities in the system, such as this stage depends on temporal coincidence and is indepen- spike generating mechanisms, in determining the measured dent of the phase of the image signal. neuronal response, especially at high stimulus contrasts. The responses of small-field DS neurons in the optic lobes Since the multiplication stage of the Reichardt model and of insects to moving sinusoidal gratings oscillate around the squaring stage of the Energy model are both forms of the mean response level at the fundamental and second quadratic non-linearity, it is difficult to distinguish direct and harmonic frequencies of the stimulus temporal frequency indirect models at the physiological and behavioral levels. (DeVoe, 1980). Similar fundamental and second harmonic For example, in their mathematically perfect forms, the En- responses can be observed in the responses of wide-field ergy and Reichardt models produce identical outputs (van neurons if the gratings are presented in restricted areas of Santen and Sperling, 1985; Emerson et al., 1992), since AB a cell’s visual field (Egelhaaf et al., 1989; Ibbotson et al., = [(A + B)2 − (A − B)2]/4. However, biological systems 1991). The amplitude of oscillation at higher-order harmon- operating according to these two principles are not indistin- ics was found to be negligible, implying that a second-order guishable at all stages. Emerson et al. (1992) recorded the (quadratic) non-linear interaction occurs in the elementary responses of complex cells in cat striate cortex thought to motion detectors feeding into the neurons. Similar experi- be elements within the sub-units of the Energy model. By C.W.G. Clifford, M.R. Ibbotson / Progress in Neurobiology 68 (2003) 409–437 417

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

Fig. 9. Responses to apparent motion of On–Off-DS cells in the rabbit retina. In the following account, the stimulus consists of two adjacent recording the response to two bars displaced in space and bar stimuli (labeled slit-A and -B) placed in the cell’s receptive field and time and subtracting off the responses to the bars presented oriented perpendicular to the cell’s preferred direction. The cell’s preferred individually, Emerson et al. (1992) were able to calculate direction is from slit-A to -B. In all cases, the expected response to the the non-linear interaction of the two bars as a function of first slit has been subtracted. (A) Responses to apparent motion in the null-direction. The upper curve shows the average response vs. contrast their spatial and temporal displacement. The data revealed function generated when slit-A was presented alone (curve marked 0%). space–time oriented two-bar interaction fields that could be Other curves show the functions produced when the motion sequence simulated by spatially integrating the outputs of motion en- was slit-B then slit-A (slit-B contrasts are shown alongside the respective ergy sub-units but could not be simulated by any stage of curves). As the contrast of slit-B increased, the response vs. contrast the Reichardt detector. function produced by subsequent presentation of slit-A was more strongly attenuated compared to stimulation of slit-A alone. (B) Response vs. contrast curves for apparent motion in the preferred direction. The dashed 2.4.2. Inhibition line shows the response function generated by stimulating slit-B alone. In the Reichardt and Energy models, the essential Other curves show functions obtained during apparent motion from slit-A non-linearity is facilitatory. To explain the responses of to -B (slit-A contrasts were 0, 10, 20 and 30%, as illustrated). Increasing On–Off-DS retinal ganglion cells in the rabbit, Barlow and the contrast of slit-A additively enhances the response elicited by the second slit, so curves are shifted upwards in parallel. Adapted from Fig. 10 Levick (1965) proposed two mechanisms, a facilitatory and of Grzywacz and Amthor (1993) and Fig. 10 of Amthor and Grzywacz an inhibitory model. The facilitatory model was conceptu- (1993). ally similar to a single sub-unit in the Reichardt detector, while under the inhibitory model responses in one direction were selectively inhibited. The inhibitory scheme has been a non-linear division-like process similar to that expected modeled at the synaptic level using shunting inhibition from the inhibitory scheme (Fig. 9). (Torre and Poggio, 1978; Koch et al., 1983). Inhibition is Subsequently, Holt and Koch (1997) have shown generated by increasing the membrane conductance of a that shunting inhibition has a divisive effect only on neuron and shunting incoming currents out from the cell. sub-threshold excitatory post-synaptic potential amplitudes. This interaction is division-like because shunting inhibition Shunting inhibition actually has a subtractive effect on the divides the excitatory currents by the membrane conduc- firing rate in most circumstances because the spiking mech- tance. The inhibitory model was tested by recording the anism appears to clamp the somatic membrane potential to responses of On–Off-DS cells in the rabbit retina to appar- a level above the resting potential. Consequently, the cur- ent motion in the preferred (Amthor and Grzywacz, 1993) rent through the shunting conductance is independent of the and null directions (Grzywacz and Amthor, 1993). While firing rate, which leads to a subtractive rather than a divi- preferred direction excitation produced linear facilitation, sive effect. Holt and Koch (1997) suggest that observation null direction inhibition appeared to be characteristic of of divisive inhibition in the spiking properties of cortical 418 C.W.G. Clifford, M.R. Ibbotson / Progress in Neurobiology 68 (2003) 409–437 neurons might be due to effects generated at the network rather than the synaptic level. Given the complex circuitry that forms the input to retinal ganglion cells in the rabbit (see Section 2.1 and Fig. 12), such network effects might underlie the division-like inhibition observed by Grzywacz and Amthor (1993). Direction-selective responses have been driven in rabbit On–Off-DS ganglion cells by edges of light moving only 1.1 ␮m (26 of visual angle) across the retina (Grzywacz et al., 1994). This distance is smaller than the spacing between rabbit photoreceptors, which is approximately 1.9 ␮mor46 (Young and Vaney, 1991). It is suggested that this directional hyperacuity is the result of Fig. 10. Temporal frequency tuning vs. speed tuning. (A) Schematic spatio-temporal frequency response function for a speed-tuned neuron. low-noise high-gain signal transmission from the photore- Iso-response contours have their major axis lying along an iso-speed line ceptors to the ganglion cells. Moreover, the result suggests (dotted). For all spatial frequencies, the peak response is obtained at the that directional selectivity can be generated in small por- same speed. (B) Schematic spatio-temporal frequency response function tions of the dendritic processes of ganglion cells and does for a temporal frequency-tuned neuron. The spatio-temporal frequency re- not require a whole cell mechanism. sponse function is the product of separable spatial and temporal frequency response functions. Iso-response contours have their major axis parallel The intracellular mechanisms leading to the generation to the spatial or temporal frequency axis. For all spatial frequencies, the of direction-selective responses in rabbit On–Off-DS cells peak response is obtained at the same temporal frequency. have been studied using patch clamp recording (Taylor et al., 2000). Taylor et al. first showed that movement in the cell’s preferred direction caused a greater excitatory current to when a cell is speed tuned (Fig. 10A). The orientation of enter the cell’s dendrites. They then voltage-clamped the the ridge corresponds to a particular speed of image mo- dendritic membrane at −70 and −30 mV and recorded the tion. Alternatively, if the cell is not tuned to speed but rather synaptic currents produced by a moving bar. The difference to specific spatial and temporal frequencies, as in the final between the synaptic currents generated by preferred and output of the Reichardt and Energy models, we would ex- null direction motion was more pronounced when the cell pect a response profile with elliptical contours whose ma- was more depolarized (−30 mV) and was predominantly jor axes are parallel to the spatial and temporal frequency caused by an increase in inhibition for null direction motion. axes (Fig. 10B). For a diagonally oriented ridge, space and When the intracellular concentration of chloride was equili- time are inseparable, meaning that the cell’s response profile brated to the extracellular level, making the reversal potential is not simply the product of separate spatial and temporal of ␥-aminobutyric acid A (GABAA) receptor-mediated inhi- filters. bition equal to that of excitation, this difference disappeared. An alternative theoretical approach to motion detection is From this, it was concluded that a major component of the provided by the gradient model (Fennema and Thompson, direction selectivity of DS retinal cells in the rabbit is gen- 1978; Horn and Schunk, 1981; Srinivasan, 1990; Johnston erated by null-direction inhibition acting post-synaptically et al., 1992). Gradient-based approaches use filters that take to the ganglion cell dendrites (Taylor et al., 2000). How- “fuzzy derivatives” (Koenderink and van Doorn, 1987), ever, contrary to the findings for rabbit On–Off-DS cells, blurring and differentiating the image, and combine the fil- Borg-Graham (2001) found that DS retinal ganglion cells ter outputs as a quotient of temporal and spatial derivatives in the turtle were not the site of the non-linear interaction to estimate velocity (Fennema and Thompson, 1978; Horn and that direction-selective coding probably occurred earlier and Schunk, 1981; Johnston et al., 1992, 1999; Johnston in the visual system. Details of motion processing prior to and Clifford, 1995a). Under this scheme, motion is com- retinal ganglion cells in the turtle are given in Section 3.1 puted from the ratio of temporal and spatial frequency-tuned (DeVoe et al., 1989). Borg-Graham (2001) questioned the channels. The spatial and temporal frequency-tuned mech- assumption that chloride loading simply transforms all in- anisms in the gradient model are non-directional but their hibitory inputs to excitatory ones while leaving the original combination at the division stage produces a directional, excitatory inputs unchanged. He suggested that the effects speed-tuned response. The division operation can be thought of high intracellular chloride might be more complex, cast- of as a form of inhibitory non-linearity. ing doubt on the interpretation of ganglion cell dendrites as Directionality and speed-tuning emerge from the gradient the site of the non-linear interaction in the rabbit retina. scheme at a later stage than temporal frequency tuning. This is in contrast to the Reichardt model where speed-tuning 2.4.3. Speed-tuned motion detectors at the sub-unit stage is replaced by temporal frequency In a spatiotemporal frequency response profile, where tuning at the motion opponent stage (Zanker et al., 1999). neuronal response is plotted as a function of spatial fre- Although a speed-tuned signal is available from the output quency on the abscissa and temporal frequency on the ordi- of a Reichardt sub-unit, the signal is superimposed on a nate, there are diagonally oriented ridges of peak sensitivity large non-motion related signal making it an impractical C.W.G. Clifford, M.R. Ibbotson / Progress in Neurobiology 68 (2003) 409–437 419 site to extract speed information. The Energy model con- To identify decisively how image motion is computed in tains space–time oriented linear filters (Fig. 7) that are both a particular biological system it is necessary to investigate directional and temporal frequency-tuned (Emerson et al., the component elements of the system (see Section 3). The 1992). Several models have been proposed to show how differences between models in terms of directionality and speed could be extracted by analyzing the distributed output temporal frequency tuning mean that in principle they are of motion-energy sub-units tuned to selected spatial and distinguishable physiologically. Of course, one problem with temporal frequencies (Heeger, 1987; Grzywacz and Yuille, such an enterprise is knowing at which stage of the func- 1990; Simoncelli and Heeger, 1998). However, Ascher and tional motion processing hierarchy you are recording. How- Grzywacz (2000) point out that these models are often ever, given that gradient models have been shown to have based on rather theoretical filters that do not fit with exist- considerable predictive power in terms of the psychophysics ing biological data. Ascher and Grzywacz (2000) present a of motion perception in humans (Johnston and Clifford, Bayesian model for the measurement of visual velocity that 1995a,b; Benton et al., 2000, 2001), more work is warranted allows the estimation of retinal velocity with more realistic to investigate how such models might map onto the under- assumptions about the form of the spatial and temporal lying physiology. For example, Johnston et al. (1992) have filters. Importantly, the model is consistent with observed shown that stages of their Multi-channel Gradient model of aspects of speed perception such as the dependence of motion perception respond to drifting sine wave gratings in perceived speed on contrast (Thompson, 1982). a manner resembling that of some cortical simple and com- Speed-tuning is a common property of direction-selective plex cells in terms of directionality and phase independence. neurons in primate middle temporal area (Rodman and Albright, 1987; Perrone and Thiele, 2001, 2002). A small percentage of direction-selective neurons have also been 3. Evidence for the cellular mechanisms of motion identified as speed-tuned in the mammalian pretectal nu- detection cleus of the optic tract (Ibbotson and Price, 2001) and in its avian homologue, the pretectal lentiformis mesencephali 3.1. Retinal motion detectors in vertebrates (Wylie and Crowder, 2000). Spatiotemporal receptive field profiles in primate primary visual cortex (V1) are not tuned The vertebrate retina contains all of the sequential to image speed but rather to specific spatial and temporal stages and lateral connections required for motion detec- frequencies (Foster et al., 1985). Some V1 neurons have tion (Fig. 11; Dowling, 1979), although these may not be low-pass (sustained) response profiles while others are fully utilized in all species. The pathway begins with the band-pass (transient) (Foster et al., 1985; Hawken et al., photoreceptors, then a layer of bipolar cells and finally the 1996). Perrone and Thiele (2002) suggest that V1 neu- RGCs, which form the output of the retina (Fig. 11). At the rons with separable response functions provide the input interface between the photoreceptors and the bipolar cells is to speed-tuned neurons in the middle temporal area. They a layer containing horizontal cells that provide the substrate provide a model, the weighted intersection model (WIM), for lateral interactions between neighboring areas of the vi- of how the visual system extracts speed independent of sual field. The horizontal cells provide the substrate for the spatial frequency. The WIM predicts that the maximum lateral inhibition that generates center-surround interactions output of a speed-tuned middle temporal area (V5) (MT) and concentric receptive fields in bipolar cells and RGCs neuron occurs whenever it receives equal input from sus- (e.g. Kuffler, 1953). More lateral interconnections are pro- tained and transient V1 neurons. The WIM is related to vided by amacrine cells, which form a second horizontal gradient schemes of motion detection in that the response layer at the interface between the bipolar and ganglion cells. of its model MT neurons depends on the ratio of sustained As outlined in Section 2.2.1, early recordings from the and transient input. However, while gradient schemes typ- rabbit retina revealed two types of direction-selective RGCs: ically compute image speed directly from the ratio of the On- and On–Off-DS RGCs (Barlow et al., 1964; Barlow responses of transient and sustained mechanisms, the corre- and Levick, 1965). Both cell types were direction-selective sponding stage of the WIM model shows band-pass speed but the On-cells responded only to the movement of bright tuning consistent with the properties of MT neurons. edges while the On–Off-cells responded to the movement Much has been made in psychophysical circles of the of both bright and dark edges. The discovery that some mathematical equivalence of the various classes of mo- RGCs were directional provided an excellent opportunity to tion detection scheme (e.g. van Santen and Sperling, 1985; use the rabbit retina as a model system to investigate the Adelson and Bergen, 1986). For example, a least squares cellular mechanism responsible for direction-selectivity in a gradient estimator of velocity based on Gaussian derivative biological system (for review see Vaney et al., 2001). filters can be redescribed as an opponent Energy model The On–Off-DS RGCs have a bistratified morphology based on filters oriented diagonally in space–time (Adelson (Amthor et al., 1984, 1989; Oyster et al., 1993). The outer and Bergen, 1986), while Energy and Correlation models dendritic stratum receives input from Off-center neurons (de- with the same constituent filters effectively perform the same polarized by brightness decrements) and the inner dendritic computations in a different order (Emerson et al., 1992). stratum receives input from On-center cells (depolarized 420 C.W.G. Clifford, M.R. Ibbotson / Progress in Neurobiology 68 (2003) 409–437

Fig. 11. The retina has three nuclear layers: The outer nuclear layer (photoreceptors), the inner nuclear layer (bipolar, horizontal and amacrine cells) and the ganglion cell layer (ganglions). Between the inner and outer nuclear layers is the outer plexiform layer where lateral connections are formed between photoreceptors, bipolar cells and horizontal cell processes. Between the inner nuclear layer and the ganglion cell layer is the inner plexiform layer where lateral connections are formed between bipolar, amacrine and ganglion cells. Information flows from photoreceptors to ganglion cells but there are also many lateral interactions. by brightness increments). The dendrites of the On–Off-DS cells co-stratify with cholinergic (starburst) amacrine cells (Vaney et al., 1989), which provide excitatory inputs to the ganglion 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 the On-dendrites of the On–Off-DS RGCs (Amthor et al., 1989). The main difference between the dendrites of the two RGC types appears to be that the On DS cells have receptive fields that are approximately three times wider than the On–Off-DS cells (Pu and Amthor, 1990). The On–Off-DS cells do not have anatomical features that correlate with the cells’ preferred response directions, but they do form a very dense tiling mosaic across the retina where cells of the same type establish non-overlapping spatial domains (Oyster et al., 1993; Amthor and Oyster, 1995). How do the structural elements discussed above fit into the theoretical approaches discussed in Section 2? A se- Fig. 12. Schematic of the neural circuitry thought to underlie ries of experiments has shown that amacrine cells appear direction-selectivity in the rabbit retina (adapted from Vaney et al., 2001). DS RGC, direction-selective retinal ganglion cell; Bip, bipolar cell; SA, to provide the neural substrate for some of the lateral in- starburst amacrine cell; GA, GABAergic amacrine cell. Note that there are teractions required for motion detection (Fig. 12). More many cones feeding into each DS RGC, with much summation en route. specifically, starburst amacrine cells provide the substrate This could give the impression that direction-selectivity is generated quite to potentiate the responses of ganglion cells to motion in coarsely within the receptive fields of the ganglion cells. However, edges all directions and possibly to generate preferred direction of light moving only 1.1 ␮m, which is smaller than the inter-photoreceptor distance, can generate directional responses (Grzywacz et al., 1994). This facilitation (Grzywacz and Amthor, 1993; He and Masland, result suggests low-noise high-gain signal transmission from the photore- 1997). Significant motion facilitation can arise from inputs ceptors to the ganglion cells and the generation of direction selectivity in well outside the region of retina occupied by the dendritic small portions of the dendritic processes of ganglion cells (see Section arborizations, which corresponds to the classical receptive 2.4.2). C.W.G. Clifford, M.R. Ibbotson / Progress in Neurobiology 68 (2003) 409–437 421

field, of the On–Off-RGCs for preferred direction motion tors such as the Energy model (Adelson and Bergen, 1985; but not for the anti-preferred direction (Amthor et al., Emerson et al., 1992). Anatomical evidence shows that 1996). Anti-preferred inhibition appears to arise from a some turtle cones have asymmetrically radiating teloden- different set of GABAergic amacrine cells, which again dria (Ohtsuka and Kawamata, 1990), which could provide form synapses onto the DS RGCs (Fig. 12; Grzywacz et al., the spatial asymmetry required for early motion processing 1997; Massey et al., 1997). Consequently, motion in the between photoreceptors at the level of the retina’s outer preferred and anti-preferred directions is actually driven by plexiform layer. different systems rather than identical systems with mirror In summary, the rabbit retina has revealed a neural archi- symmetric directional tuning, as predicted by theoretical tecture that contains bundles of DS ganglion cells that are motion detectors such as the Reichardt and Energy models. innervated by amacrine and bipolar terminals. This orga- Moreover, Section 2.3 has already shown that the biophys- nization provides all of the wiring required for calculating ical mechanisms underlying the response to motion in the the direction of image motion. Perhaps most interesting is preferred and anti-preferred directions are different (Amthor the finding that the actual structure of the rabbit’s retinal and Grzywacz, 1993; Grzywacz and Amthor, 1993), i.e. motion detectors does not correspond exactly with any of preferred direction motion produces linear facilitation while the theoretical models. Rather, evolution has developed a null direction motion produces non-linear inhibition (Fig. 9). system that utilizes all of the theoretical concepts described The rabbit retina has provided much information about the in the models but is organized in a way not predicted by the- cellular mechanisms of retinal motion detection but retinas in oreticians. This is certainly a lesson for physiologists who other vertebrate species have also yielded important results. attempt to find exact correlates of models in neural tissue. Perhaps the most thoroughly studied example is the turtle Rather, a broad approach is required that is guided but not retina (DeVoe et al., 1989). Although many results from the driven by the expectations of theoretical models. Another turtle retina provide evidence of a similar mechanism to the interesting observation from the turtle retina is that the be- rabbit (Marchiafava, 1979; Ariel and Adolph, 1985; Kittila ginnings of directional tuning may occur as early as the dis- and Granda, 1994; Smith et al., 1996), intriguing differences tal retina. Moreover, it is important to look for directionally have also been identified. Perhaps the most obvious is that biased responses rather than motion-opponent responses the turtle retina has On–Off, On- and Off-DS cells, while when searching for the input structures of biological motion the rabbit does not have the Off-specific type (Jensen and detectors. DeVoe, 1983). DeVoe et al. (1989) showed in the turtle that 33% of 3.2. Sub-cortical motion processing retinal ganglion cells were direction-selective. They also showed that 37% of amacrine cells and 42% of bipolar Although the lateral geniculate nucleus shows some direc- cells were directional. The retinal ganglion cells were fully tional effects (Lee et al., 1979; Thompson et al., 1994; White direction-selective, giving spikes in one direction and no et al., 2001), the most striking regions of the sub-cortical spikes in the opposite direction, suggesting strong non-linear mammalian brain in terms of direction selectivity are the mechanisms. In bipolar and amacrine cells, post-synaptic pretectum and accessory optic system (AOS) (e.g. Simpson, potentials were larger for movement in one direction than 1984). The pretectum contains the NOT and the AOS con- the opposite but the cells were not motion opponent. A small sists of three nuclei: the lateral, medial and dorsal termi- number of directional turtle horizontal cells have been iden- nal nuclei (LTN, MTN, DTN). In all of these nuclei, the tified (Adolph, 1988; DeVoe et al., 1989). They show far most commonly encountered cells are those that respond smaller changes in amplitude between opposite directions of in a highly direction-selective (motion-opponent) manner to motion than are observed in bipolar, amacrine or ganglion the movement of large regions of the visual scene. In most cells. cases, the cells give motion opponent responses in which Evidence for cholinergic and GABAergic processes in motion in one direction excites the cell while motion in the both the inner and outer plexiform layer, combined with opposite direction inhibits the cell’s spontaneous activity evidence of directional tuning even in the inner segments (Collewijn, 1975a; Hoffmann, 1989; Ibbotson et al., 1994). of a small number of cone-type photoreceptors (Carras and The NOT and AOS are connected to the motor system that DeVoe, 1991), suggests that at least some directional coding controls stabilizing eye movements such as optokinetic nys- occurs very early in the distal retina of the turtle (Criswell tagmus (Collewijn, 1975b; Schiff et al., 1988; Belknap and and Brandon, 1992). It should be noted that the directionally McCrea, 1988). The NOT and the nuclei of the AOS receive biased responses recorded in the cones of the turtle retina, direct input from the retina in all species studied (e.g. cat: as with the bipolar and amacrine cells, did not have the full Ballas and Hoffmann, 1985; monkey: Telkes et al., 2000). In directional properties associated with DS retinal ganglion some animals, such as monkey and cat, there is also an in- cells (Carras and DeVoe, 1991). Rather, responses to motion direct input from the visual cortex (cat: Schoppmann, 1981; in one direction were simply larger than those to other di- monkey: Distler et al., 2002) but in other species, such as rections (see Fig. 6), which corresponds to the expectations the marsupial opossum (Pereira et al., 2000) and wallaby of the early linear stages of certain theoretical motion detec- (Ibbotson et al., 2002), there is no cortical input. 422 C.W.G. Clifford, M.R. Ibbotson / Progress in Neurobiology 68 (2003) 409–437

The motion opponent character of the responses in these 1965) and primates (Hubel and Wiesel, 1968; Foster et al., nuclei suggests that the action potentials are generated in 1985). However, the physiology of cortical neurons has also the cells after the final subtraction stage of the motion been studied in a number of other mammals such as the rab- processing mechanism, as outlined in Section 2. This ob- bit (Murphy and Berman, 1979), opossum (Rocha-Miranda servation leaves the possibility that the final subtraction et al., 1973) and wallaby (Ibbotson and Mark, in press). The phase actually occurs in the dendrites of the NOT and AOS general finding in all the species mentioned is that directional neurons prior to the site of spike generation. The direc- responses are common in the primary visual cortex (referred tional responses in the NOT of the wallaby, which does to as area 17 or area V1). Other areas in the visual cortex are not receive input from the cortex (Ibbotson et al., 2002), known to specialize in coding motion information, notably compare very well with the final subtraction stage of both the middle temporal area (MT or V5) in primates (Dubner the Energy and Reichardt models (Ibbotson and Clifford, and Zeki, 1971) and the posteromedial lateral supersylvian 2001a,b; Ibbotson et al., 1994). It has also been established area (PMLS) in cats (Blakemore and Zumbroich, 1987). that the fundamental non-linearity in the motion process- Cells fall into three main categories: (1) non-directional; (2) ing mechanism is quadratic, as predicted by both models directionally biased (non-opponent); and (3) motion oppo- (Section 2.4.1; Ibbotson et al., 1999). The spatiotemporal nent. Cells in the first category are not directional but may response properties of neurons in the sub-cortical motion be quite strongly orientation tuned, e.g. they might respond processing areas of the avian brain suggest that similar strongly to vertically oriented gratings but not to horizontal motion detector mechanisms are in operation (Wylie and gratings (Mazer et al., 2002). Orientation tuned cells usually Crowder, 2000). Indeed, the spatiotemporal tuning of cells give their best responses when an oriented bar or grating in the avian and mammalian brain are organized in a very is moved back and forth along an axis perpendicular to the similar fashion across the cell population, suggesting that preferred orientation. The directionally biased cells tend the visual environment during head and eye movements to be orientation tuned but the response to motion in one molds the spatiotemporal properties of the neurons across direction along the preferred motion axis (which is perpen- widely separated phyla (Ibbotson and Price, 2001). dicular to the preferred orientation axis) is stronger than The retinal ganglion cells that provide the input to the the response in the opposite direction (Henry et al., 1974). NOT and AOS in vertebrates are generally slowly con- Neurons with motion opponent properties respond strongly ducting ganglion cells with small- to medium-sized cell in the preferred direction and are inhibited by motion in the bodies. These are so-called ‘specialized’ cells in primates opposite direction. It has recently been reported that, at high (Telkes et al., 2000) and W-cells in cats, rabbits and rats speeds, many neurons in the primary visual cortex of cat (Ballas et al., 1981; Pu and Amthor, 1990; Kato et al., 1992; and monkey appear to be selective for motion parallel rather Rodieck and Watanabe, 1993). It is known that these cells than perpendicular to their preferred orientation (Geisler are direction-selective in cats (Hoffmann and Stone, 1985), et al., 2001). This has been taken as support for the hy- rabbits (Oyster et al., 1972) and turtles (Rosenberg and Ariel, pothesis that spatial streaks caused by motion smear in the 1991). Ilg and Hoffmann (1993) found that most cortical image can also be used as a cue in the perception of motion cells that could be stimulated anti-dromically from the NOT (Geisler, 1999). were strongly directional. Therefore, the cortical input in The majority of cells in the LGN, which is the primary primates is also already direction-selective, as evidenced by relay between the retina and cortex, have responses that are the strong input from directional fibers from the extrastri- similar to those of center-surround retinal ganglion cells ate motion processing areas MT and MST (Hoffmann et al., (Hubel and Wiesel, 1961). Some LGN cells show weak 2002). Presumably, the role of the NOT neurons is to sum- orientation tuning and some directional effects (Lee et al., mate the inputs from directional cells to generate selective 1979; Thompson et al., 1994; White et al., 2001). However, responses to large field stimulation. Moreover, as many cells as a general statement, the LGN contains cells that have from the visual cortex are not motion opponent, the NOT much weaker orientation and directional preferences than neurons might provide the neural substrate for the final sub- the cortex. It is therefore reasonable to suggest that V1, or traction phase to produce motion opponency. The evidence at least the interface between the LGN and V1, is a good suggests that the cellular mechanisms of directional motion place to look for the cellular basis of motion detection in detection occur primarily before the NOT or AOS. Most di- the geniculo-cortical pathway. However, before discussing rectional properties probably arise in the retina but in cer- the evidence from the LGN and V1 themselves, it is worth tain species there is an additional input from higher visual considering evidence from higher areas of the cortex that centers in the cortex. receive input from V1. The most striking region of the primate brain in terms of 3.3. Cortical motion processing motion processing is the middle temporal area, MT or V5 (Dubner and Zeki, 1971). The majority of cells in MT are The visual cortex is one of the most heavily studied areas direction-selective but it does not appear to extract local of the brain. The majority of work on cortical motion pro- motion information itself but rather receives directional sig- cessing has been conducted on cats (Hubel and Wiesel, 1962, nals from earlier stages in the visual system (Livingstone C.W.G. Clifford, M.R. Ibbotson / Progress in Neurobiology 68 (2003) 409–437 423 et al., 2001). Lesions of V1 in primates greatly reduce the cells to sequentially presented neighboring bars of opposite prevalence of DS cells in MT but do not totally abolish contrast (i.e. light bar then dark bar) produced inverted the phenomenon (Rodman et al., 1989; Girard et al., 1992). responses. This result, as discussed in Section 2.2, indi- MT also appears to receive directional inputs directly from cates that the polarities of the signals entering the motion sub-cortical structures such as the colliculus and pulvinar detectors are preserved. Retinal ganglion cells, LGN neu- (Rodman et al., 1990; Bender, 1982; Beckers and Zeki, rons and simple cells in the visual cortex show inverted 1995). Movshon and Newsome (1996) recorded from V1 responses to opposite contrasts while complex cells in the neurons that could be anti-dromically activated by electrical cortex do not. Therefore, Livingstone et al. (2001) suggest stimulation of MT. They found that these cells were already that direction-selectivity is generated within or between directionally biased. The evidence therefore points towards geniculate inputs or simple cells. Movshon and Newsome V1 as a major location for motion computation. It has been (1996) found that the V1 neurons that projected directly to proposed that directional V1 cells are local motion energy MT were of the special-complex type, i.e. they responded filters (Adelson and Bergen, 1985; Heeger, 1987; Grzywacz to a broad range of spatial and temporal frequencies and and Yuille, 1990; DeValois et al., 2000). As outlined in were sensitive to very low contrasts. It is probable that the Section 2, there is evidence to suggest that certain simple special complex cells receive their input from directional and complex cells in cat V1 have directional properties simple cells or directly from LGN fibers or both (Fig. 13). that are similar to those predicted by stages of the Energy Evidence suggests that the spatial separation between model (Emerson et al., 1992; Emerson, 1997; Emerson and inputs arises from the differences in the receptive field Huang, 1997). locations of neighboring LGN or simple cells. DeValois Livingstone et al. (2001) recorded from MT neurons et al. (2000) find that directional V1 cells in the macaque in primates and showed that directionality occurred for monkey get the inputs required for motion detection by sequential presentation of stimuli less than 1/10 of a de- combining signals from two identified sub-populations of gree apart. This distance is far smaller than the receptive non-directional cortical neurons. These sub-populations field sizes of most directional V1 cells. The implication differ in the spatial phases of their receptive fields. That is, is that interactions are most probably occurring between both cell types have On and Off zones but corresponding sub-regions or -units within the receptive fields of V1 cells zones are spatially displaced with respect to each other. The (Fig. 13). It was also established that responses of MT two sub-populations also have distinct temporal properties: those with a slow monophasic temporal response and those with a fast biphasic temporal response. The fast biphasic cells cross over from one response phase to the reverse just as the monophasic cells reach their peak response. This 90◦ (quadrature) phase difference would make the two sub-populations of cells ideal building blocks for the Energy model. Where might be the origin of the temporal differences in the non-directional V1 sub-populations identified by DeValois 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 two non-directional cortical sub-populations they identify might receive their input from parvo and magno LGN cells, re- spectively. In the cat, X- and Y-relay cells in the LGN have been classified as lagged or non-lagged (Mastronarde, 1987; Mastronarde et al., 1991; Humphrey and Weller, 1988). When stimulating with sinusoidally luminance-modulated Fig. 13. Schematic of the flow of information from lateral geniculate stimuli, lagged and non-lagged cells fire about a quarter of nucleus (LGN) to the middle temporal area (MT) via the primary visual < cortex (V1). The LGN has lagged and non-lagged cells that feed into a cycle out of phase at low temporal frequencies ( 4 Hz). V1 cells. In primate V1, non-directional lagged and unlagged cells have Saul and Humphrey (1990) simulated the input of lagged been identified (DeValois et al., 2000). In cat, cells with lagged and and non-lagged cells onto cortical neurons and showed that unlagged zones have been found (Saul and Humphrey, 1992). In primates, the responses would be direction-selective at low temporal it would appear that V1 cells then feed into other V1 neurons such as frequencies but would lose that directionality at higher fre- special complex cells. However, the special complex cells may also receive direct input from the LGN. By the time information reaches MT it has quencies (>4 Hz). Saul and Humphrey (1992) went on to been processed so that very specific movement information is integrated use sinusoidally luminance-modulated stimuli in cortex to together. search for signs of lagged and non-lagged inputs to cortical 424 C.W.G. Clifford, M.R. Ibbotson / Progress in Neurobiology 68 (2003) 409–437 neurons. They found lagged and non-lagged zones within tems of other insects have also contributed to the field (e.g. the receptive fields of the cortical neurons. The distribution bees: Ibbotson, 1991a; moths: Milde, 1993; locusts: Osorio, of latency and absolute phase across the sample of cortical 1986). The lobula complex in flies is divided into two com- simple cells was similar to that found in the LGN. The partments. The most dorsal compartment is referred to as similarity between cortex and LGN was greatest in the the lobula plate and it contains direction-selective neurons geniculate recipient layers of the cortex. (reviewed by Hausen, 1993) and has been described as a In conclusion, the results from various mammals indicate tectum-like structure (Douglass and Strausfeld, 2001). The that the cortex contains a plethora of cell types within the neurons in the lobula plate transfer information from the op- very general categories of simple and complex cells. The tic lobes into the midbrain and are involved in controlling op- smallest image displacement that leads to a directional re- tomotor responses. Optomotor responses are reflexive head sponse is considerably smaller than the receptive field size of and body movements that attempt to stabilize the retinal im- most simple cells, suggesting that the essential lateral inter- age and control body orientation during walking and flight. actions may occur between the terminals of LGN neurons. The lobula plate functions in a similar fashion to the nuclei Subsequent processing progresses from linear summation of of the AOS and pretectal NOT in mammals (see Section 3.2). signals in some LGN neurons up to highly directional re- Extensive studies on the neurons of the lobula plate show sponses in the special complex cells that project to MT (in that these cells have response properties very similar to those the case of primates). It is clear that far less is known at the expected from the final subtraction stages of the Reichardt cellular level in the cortex than in the retina. However, the and Energy models (Egelhaaf et al., 1989), as is the case general architecture of the cortical system is slowly being in the AOS and NOT of mammals (Ibbotson et al., 1994; revealed. Ibbotson and Clifford, 2001a,b). That is, signals are already motion opponent. Elementary motion detector units in the 3.4. Motion detectors in insect optic lobes fly can be excited by stimulation of just two receptor cells in adjacent facets of the eye (Kirschfeld, 1972; Franceschini As alluded to in Section 2, a great deal of information re- et al., 1989). The discrete nature of local motion interactions lating to motion detection has arisen from work on insects, in insects should assist in tracing movement signals at the including the classic work of Hassenstein and Reichardt electrophysiological and anatomical levels. Given the clear (1956). It is interesting to look at the mechanisms of motion response properties in the lobula plate and the highly repet- detection at the cellular level in insect optic lobes, which itive and organized nature of the medulla and lamina, it is are made up of three neuropiles (Fig. 14). Starting just be- interesting to search for the elements of the optic lobes that low the retina and working inwards towards the brain, these provide the input to the lobula plate neurons. This search has neuropiles are the lamina, medulla and lobula complex. The the very real possibility of identifying the biological build- most thoroughly studied insect visual system is that found ing blocks of elementary motion detectors (DeVoe, 1980; in flies (Douglass and Strausfeld, 2001) but the nervous sys- DeVoe and Ockleford, 1976; Gilbert et al., 1991).

Fig. 14. Schematic of connections in the insect optic lobes. All elements shown are thought to be involved in motion detection. PR, photoreceptors; L2 and 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 because their 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. C.W.G. Clifford, M.R. Ibbotson / Progress in Neurobiology 68 (2003) 409–437 425

Movement-specific responses have not been identified The extracellular electrophysiology certainly points to- from cells in the lamina (Mimura, 1974) but responses are wards the medulla or the interface between the lamina and highly direction-selective in the lobula plate. The elements medulla as a likely source for motion detector interactions. responsible for direction-selectivity must, therefore, be lo- What do we know of the anatomy of those areas of the optic cated in the circuitry between the output synapses of the lobe? The main inputs to T4 and T5 cells appear to arise from lamina cells and the input synapses of the lobula plate. In the transmedullary cells Tm1, iTm and, in certain species, the following account, we will start in the lobula plate (the from Tm1a, Tm1b and Tm9 (Fischbach and Dittrich, 1989; most directional area) and move outwards towards the lam- Douglass and Strausfeld, 1998). It is thought that the termi- ina (the least directional area). The dendrites of the lobula nals of the iTm cells are presynaptic to the dendrites of T4 plate neurons receive retinotopically organized synaptic in- cells and that the terminals of Tm1, Tm1a, Tm1b and Tm9 puts from T4 and T5 neurons (Strausfeld and Lee, 1991). cells are presynaptic to the dendrites of T5 cells (Fig. 14). The T4 neurons have their dendrites in the medulla while Physiological evidence shows that some Tm cells are direc- the dendrites of the T5 cells reside in the outer stratum of tional but that the responses are not fully motion opponent the lobula (Fig. 14). It has been suggested that the T4 and (Gilbert et al., 1991; Douglass and Strausfeld, 1995). It is T5 cells are functionally similar to retinal ganglion cells in probable that the Tm1 cells are one of the components that mammals (Douglass and Strausfeld, 2001). The terminals make up the elementary motion detectors proposed in the of the T4 and T5 cells in the lobula plate are located in four Reichardt and/or Energy models. main levels that have distinct preferred motion directions. It is of course essential that some type of lateral spatial T5 cells generate motion opponent responses to moving interaction occur in the motion detectors between neighbor- patterns that are similar to those of the lobula plate neurons ing regions of the visual field. There are several possible (Douglass and Strausfeld, 1995). The receptive fields of T5 sources of lateral interactions. Firstly, it appears that the cells are, however, far smaller than those of lobula plate lateral interactions might occur at the transition from the neurons. In contrast, T4 cells are only weakly directional lamina to the medulla. The lamina contains several types (Douglass and Strausfeld, 1996). The T5 cells must either of L-monopolar cells in each optic cartridge, the latter con- receive input from cells that are already post-synaptic to the taining all the neural tissue that lies underneath each facet final subtraction stage of the EMDs or their input synapses lens of the eye. L2 monopolar cells may provide inputs would have to form the neural substrate for that final sub- from adjacent optic cartridges to Tm1 neurons via lateral traction. T4 cells may represent a non-opponent stage in connections involving T1 basket cells and lamina amacrine the motion detection mechanism. cells (Fig. 14). T1 cells terminate in the medulla between Recordings from unidentified cells in the medulla the terminals of L2 monopolar cells and the dendrites of have shown that there are directional and non-directional Tm1 cells and receive input from lamina amacrine cells, movement-sensitive elements (McCann and Dill, 1969; which receive their signals from several photoreceptors in Mimura, 1971; DeVoe and Ockleford, 1976; DeVoe, 1980). different cartridges (Douglass and Strausfeld, 2001). Sec- DeVoe (1980) recorded from cells in the medulla that re- ond, in the lamina, the L4 monopolar cells and the lamina sponded to moving gratings with maintained non-directional amacrine cells (Fig. 14) provide a system of connections be- depolarizations but often had directional oscillations or tween retinotopic columns (Strausfeld and Campos-Ortega, spikes superimposed on the depolarized signal. This type 1973). In this case, the L4 neurons receive input from lam- of response may arise from a cell that forms part of the ina amacrine cells and then distribute this information to L2 building block of an elementary motion detector. DeVoe and Tm1 neurons in different columns. Finally, the C2 neu- (1980) suggested that the characteristic response wave- ron provides feedback from the inner layer of the medulla forms could be explained by multiplicative inputs from back to the outer medulla and lamina (Strausfeld, 1976). lamina and medulla cells to the movement detector units. The C2 neurons cross between retinotopic columns, thus However, no anatomical evidence was then available to providing another possible source for lateral interactions in confirm this pathway. A major indicator for this idea was the motion processing pathway (Fig. 14). that motion in one direction in some medulla neurons pro- Visually responsive neurons that send their axons from duced clear second harmonic response components while the midbrain area back, centrifugally, into the medulla have motion in the opposite direction produced either no second been identified in several insects (e.g. moths: Collett, 1970, harmonics or low-amplitude second harmonic components. 1971; Milde, 1993; butterflies: Ibbotson et al., 1991). In As outlined in Section 2.4.1, the existence of second har- both moths and butterflies the dendrites of the centrifugal monics in the responses to moving gratings suggests a neurons are in the midbrain area occupied by the outputs of second-order non-linear mechanism in the motion detector. direction-selective neurons from the lobula plate. The large This is an expectation predicted for the components of both centrifugal neurons are highly direction-selective, so fully the Reichardt and Energy models (Egelhaaf et al., 1989; motion-opponent signals from the midbrain are sent into the Emerson et al., 1992). Gilbert et al. (1991) suggest that the distal layers of the medulla. This is of significance for any second-order non-linearity may occur between the outputs physiologist recording from small centrally directed neurons of L2 laminar neurons and medulla cells. in the medulla because any observed directionality may be 426 C.W.G. Clifford, M.R. Ibbotson / Progress in Neurobiology 68 (2003) 409–437 the result of signals fed back from the midbrain rather than from the adapted stimulus in the response of the population signals arising from elementary motion detectors. of cells sensitive to the adapted stimulus dimension, giving In conclusion, the insect optic lobes provide a neural sub- rise to a perceptual repulsion effect. So, for example, after strate that has the potential to reveal the exact structure of the adaptation to a downwards-moving pattern, a static pattern local motion detector networks in a biological system. Far will appear to drift upwards due to fatiguing of cells prefer- more attention has been given to the large direction-selective ring downwards motion (Wohlgemuth, 1911; Mather et al., output neurons of the optic lobes than to the complex neu- 1998). ral networks that probably form their input. It is certainly Motion adaptation impairs the ability to detect subsequent technically difficult to record from the small neurons in the motion in a direction-selective manner, such that motion co- medulla but the rewards of doing so could be substantial. herence thresholds are maximally elevated in the adapting It would be greatly beneficial to follow the course taken by direction (Raymond, 1993a; Hol and Treue, 2001). These the pioneers that have attempted to record from the motion threshold elevations show complete inter-ocular transfer, detector pathways in the insect medulla (e.g. DeVoe and demonstrating that they are of cortical origin (Raymond, Ockleford, 1976; DeVoe, 1980; Osorio, 1986; Douglass and 1993b). It is generally assumed that motion detection is Strausfeld, 1995, 1996, 2001). However, in trying to find determined by the responsiveness of the neuron most sen- the elementary motion detectors in the outer optic lobes we sitive to the test direction. As a result of adaptation, the must be careful to take into account any feedback systems responsiveness of neurons tuned to the adapting direction is that provide fully motion opponent signals to the outer optic reduced most, with the reduction in responsiveness of any lobes from the midbrain, as found in butterflies (Ibbotson given neuron determined by the angle between the adapt- et al., 1991). ing stimulus direction and that neuron’s preferred direction (Kohn et al., 2001).

4. Adaptive mechanisms in motion detection 4.2. Function or fatigue?

4.1. Perceptual consequences of motion adaptation The importance of light adaptation by photoreceptor cells in the retina is well established, enabling our visual systems Visual analysis of the world is an active process involv- to operate in a vast range of conditions from near darkness ing the continual adaptation of elementary processing units. to bright sunlight (Barlow, 1969; Laughlin, 1994). Adapta- Rapid neural adaptation is a fundamental property of vi- tion at subsequent stages of the visual system is also well sion with moment-to-moment relevance to our perception documented, but has often been viewed as a limitation of the (Muller et al., 1999; Dragoi et al., 2002). Prolonged adap- system associated with neural fatigue (Kohler and Wallach, tation to a moving stimulus has profound perceptual conse- 1944; Sutherland, 1961). However, physiological data from quences. When fixation is transferred to a stationary pattern, area 17 of cat show that adaptive contrast gain control mech- illusory motion is seen in the direction opposite to the adapt- anisms operate at a cortical level (Ohzawa et al., 1982), and ing motion, but with little or no accompanying change in suggest that transient temporal mechanisms might adapt perceived position (Nishida and Johnston, 1999; Snowden, on the basis of stimulus motion or temporal modulation to 1998). This “motion aftereffect” (MAE) has been known improve temporal frequency discrimination (Maddess et al., since ancient Greece, and has been studied extensively over 1988). Physiological studies on the pattern-specificity of the the past 40 years (see Wade and Verstraten, 1998). Motion neuronal response to motion adaptation (Hammond et al., adaptation also affects the subsequent perception of moving 1989; Saul and Cynader, 1989) and demonstrations of the stimuli, causing shifts in perceived direction (Levinson and storage of aftereffects (Wohlgemuth, 1911; Wiesenfelder Sekuler, 1976; Patterson and Becker, 1996; Schrater and and Blake, 1992) provide further evidence that there is more Simoncelli, 1998; Rauber and Treue, 1999; Alais and Blake, to motion adaptation than neural fatigue. 1999) and speed (Goldstein, 1957; Carlson, 1962; Rapoport, If cortical adaptation cannot be attributed to neural fatigue, 1964; Thompson, 1981; Smith and Hammond, 1985; Muller it is reasonable to ask whether it serves a function analogous and Greenlee, 1994; Clifford and Langley, 1996a; Bex et al., to light adaptation in the retina. The retina codes varia- 1999; Clifford and Wenderoth, 1999; Hammett et al., 2000). tions in luminance by adapting to, and hence discounting, In addition, the effect selectively impairs the ability to detect the mean luminance. Light adaptation has clear functional low contrast (Levinson and Sekuler, 1980) or incoherent benefits in ecological terms, allowing the visual system to motion (Raymond, 1993a,b; Hol and Treue, 2001). operate over a huge range of light levels. While information An early account of the neural basis of perceptual after- about the illuminant is discarded, this is of little relevance effects was that adaptation satiates or fatigues cells sensi- in comparison to the preservation of luminance changes tive to the adapting stimulus (Kohler and Wallach, 1944; carrying information about the structure of the environment. Sutherland, 1961). When the period of adaptation ceases, However, when one considers motion adaptation rather than the spontaneous discharge of the fatigued cells remains sup- light adaptation, the appropriateness of such a strategy is less pressed (Barlow and Hill, 1963). This produces a bias away clear. C.W.G. Clifford, M.R. Ibbotson / Progress in Neurobiology 68 (2003) 409–437 427

It has been argued that, in flying insects, it is more discarding information about absolute motion seems much important for motion-sensitive neurons involved in the sta- higher than the cost of losing information about the mean bilization of flight to optimize their sensitivity to changes light level. Intriguingly, a recent report by Fairhall et al. in image motion rather than to provide an accurate measure (2001) suggests that rapid adaptation of the input/output re- of absolute speed (Shi and Horridge, 1991). The logic lationship of the fly H1 neuron to different distributions of of this argument is that, to maintain stability in flight, it stimulus motion need not necessarily mean that information is more important to be able to detect small perturbations about the adapting level be discarded. Instead, Fairhall et al. in trajectory rather than to be continually reminded of the (2001) find that information about the statistics of the stim- speed of flight. It is important to realize that this argument ulus ensemble are encoded by the statistics of the interspike only holds for the stabilization-system. For other behaviors, interval distribution on a timescale only slightly longer than such as measuring the distance traveled during foraging, that of rapid adaptation. insects use information on the absolute speed of optic flow during forward flight (Esch et al., 2001; Srinivasan 4.3. Informational basis of motion adaptation et al., 2000). It is probable that insects use both absolute speed information and changes in speed to obtain their full From an information-processing standpoint, a possible repertoire of actions. Different types of cell specialized for function of motion adaptation is to work towards the ro- forward flight detection have been identified, some show- bust and efficient transmission of signals coding for image ing phasic response properties, presumably for detecting motion. The constraints on neural information transmis- changes in speed (Ibbotson, 1991a, 1992), while others sion are that signals must be passed through channels of maintain a steady firing rate during stimulation (Ibbotson, limited bandwidth that are subject to transmission errors 1991b). Recordings from the neurons that continue to (Attneave, 1954; Barlow, 1961; Laughlin, 1989; Clifford fire throughout a period of stimulation have shown that and Langley, 1996a). These limitations are analogous to the mean level of the response decreases, corresponding those faced in telecommunications applications where it is to a drop in absolute sensitivity to motion (Ibbotson and often advantageous to code signals adaptively so that the Goodman, 1990; Ibbotson, 1992; Maddess and Laughlin, best compromise can be reached between maximizing effec- 1985). At the same time, there is an increase in the tive bandwidth and minimizing the effects of transmission magnitude of changes in response to variation in image errors. Consequently, functional ideas about adaptation have motion around the adapting level (Fig. 15), correspond- been motivated by two main considerations: self-calibration ing to an improvement in differential motion sensitivity and dynamic range optimization. Self-calibration is the (Maddess and Laughlin, 1985; Maddess et al., 1991; Shi and property of a system to change itself in response to changes Horridge, 1991). in the environment (recalibration) and to adjust to pertur- In mammalian vision, one can think of situations where bations within the system in an unchanging environment sacrificing information about absolute motion for enhanced (error-correction) (Andrews, 1964; Rushton, 1965; Ullman differential motion sensitivity would be advantageous; a bear and Schechtman, 1982). Dynamic range optimization tends fishing in a stream, for example, could use differences in to reduce redundancy in the responses of individual sensory the speed of motion to detect the presence of its prey. But neurons (Attneave, 1954; Barlow, 1961, 2001), maximiz- there are also situations, as with insects, where accurate es- ing the effective bandwidth available for the transmission timation of absolute motion appears important; e.g. in pre- of novel information about the stimulus (Srinivasan et al., dicting the trajectories of moving objects and the guidance 1982; Laughlin, 1989; Clifford and Langley, 1996a). Empir- of pursuit eye movements (Priebe et al., 2001). Thus, while ical support for these ideas comes from electrophysiological the benefits of enhanced sensitivity to changes in luminance studies of the fly H1 neuron which have shown formally and changes in motion might appear analogous, the cost of that adaptation to motion tends to maximize information

Fig. 15. Neuronal model of the effect of adaptation on absolute and differential sensitivity to the speed of image motion. (A) Adaptation produces a rightward shift of the response function. (B) This rightwards shift causes the neuronal response to drop over time. (C) The rightward shift positions a steeper part of the response function at the speed of the adapting stimulus, thus increasing the amount by which the response changes for a given change in speed. 428 C.W.G. Clifford, M.R. Ibbotson / Progress in Neurobiology 68 (2003) 409–437 transmission (Brenner et al., 2000; Fairhall et al., 2001). Human psychophysical studies have shown that perceived The principle of redundancy reduction can be extended speed is affected by prior adaptation to motion (Thompson, from single neurons to populations of neurons by adaptively 1981; Smith, 1987), and even to stationary stimuli (Held and decorrelating (Barlow and Foldiak, 1989) or orthogonaliz- White, 1959; Clifford and Wenderoth, 1999). When adapt- ing (Kohonen and Oja, 1976) their responses, and may be ing and test stimuli have the same contrast, speed and direc- applicable to the coding of motion in human visual cortex tion, perceived speed is consistently decreased by adaptation (Clifford et al., 2000; Clifford, 2002). (Carlson, 1962; Rapoport, 1964; Thompson, 1981; Muller and Greenlee, 1994). Correspondingly, the perceived speed 4.4. Dynamics of motion adaptation of a constantly moving stimulus decreases as a function of adaptation duration (Goldstein, 1957), decaying exponen- Adaptation to motion has been shown to generate large, tially to a steady-level (Clifford and Langley, 1996b; Bex robust aftereffects with identified neural correlates in the et al., 1999; Hammett et al., 2000). As the perceived speed of cat (Hammond et al., 1988; Giaschi et al., 1993), monkey a constantly moving stimulus decreases, sensitivity to mod- (Petersen et al., 1985; van Wezel and Britten, 2002) and ulations or increments in speed is enhanced (Clifford and human cortex (Tootell et al., 1995; He et al., 1998; Culham Langley, 1996b; Bex et al., 1999; Clifford and Wenderoth, et al., 1999; Huk et al., 2001). The principal neural sub- 1999), at least for luminance-defined motion (Kristjansson, strate of the MAE in human visual cortex is believed to 2001), suggesting that an accurate representation of absolute be the human homologue of monkey area MT (Fig. 16; speed is sacrificed for greater differential sensitivity. Speed Tootell et al., 1995; He et al., 1998; Culham et al., 1999; increment thresholds remain approximately proportional to Huk et al., 2001), in which the vast majority of neurons perceived speed during adaptation and recovery from adap- are strongly direction-selective (Albright et al., 1984). A tation (Bex et al., 1999; Clifford and Wenderoth, 1999)so behavioral analogue of the MAE has also been observed that, as perceived speed decreases through exposure, the in the optomotor response of the blowfly (Srinivasan and ability to detect small changes around that speed improves. Dvorak, 1979). Neural correlates of motion adaptation sim- The psychophysics of human motion adaptation paral- ilar to those observed in the mammalian cortex have been lels closely electrophysiological data recorded from the observed in direction-selective neurons in a range of in- direction-selective H1 neuron in the lobula plate of the fly sects (fly: Maddess and Laughlin, 1985; bee: Ibbotson and (see Section 3.4) in both form and time course (Clifford Goodman, 1990; butterfly: Maddess et al., 1991). and Langley, 1996b). In the fly, prolonged exposure to

Fig. 16. The motion aftereffect and human area MT. (a) Stationary view of the stimulus used by Tootell 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 scale varying 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 adaptation stimulus moving continuously in a single direction. Reprinted with permission from Nature (Tootell et al., 1995). Macmillan Magazines Limited (© 1995). C.W.G. Clifford, M.R. Ibbotson / Progress in Neurobiology 68 (2003) 409–437 429 maintained motion causes the response of H1 to decay ex- ponentially over time to a steady level. The time constant of the response decay is of the order of 2–3 s for stimuli moving at around 50◦ per second (Maddess and Laughlin, 1985). In human vision, reported time constants range from 1 to 16 s dependent upon stimulus parameters (Clifford and Langley, 1996b; Bex et al., 1999; Hammett et al., 2000).

4.5. Directionality of motion adaptation

Clifford and Wenderoth (1999) found that adaptation to motion per se is not required to enhance differential speed sensitivity in humans. Adaptation to temporal modulation in the absence of net motion was found to produce signifi- cant improvements in discrimination around the subjective matching speed. Discrimination thresholds were found to decrease in proportion to perceived speed, regardless of the Fig. 17. Motion-dependence and direction-selectivity of adaptation. Nor- direction of motion or orientation of the flickering grating. malized decay time constants of the impulse responses of the insect H1 Thus, it appears that, in human vision, enhancements in dif- neuron (black) and DS neurons in the NOT (gray) compared with human psychophysical perceived speed data (hashed). The response of the insect ferential speed sensitivity are driven largely by adaptation H1 neuron is adapted to a similar degree by preferred direction motion, to temporal modulation rather than to motion itself. anti-preferred motion and flicker (Borst and Egelhaaf, 1987). The human Curiously, the finding that motion adaptation is driven by perceived speed data follows a similar pattern (Clifford and Wenderoth, temporal modulation rather than motion per se is analogous 1999). In contrast, the response of the NOT neurons is much more strongly to electrophysiological data from the H1 and HSE neurons adapted by preferred direction motion than by anti-preferred motion or flicker (Clifford et al., 1997; Ibbotson et al., 1998). of the fly lobula plate (Borst and Egelhaaf, 1987) but dis- tinct from those for motion-sensitive neurons in wallaby NOT (Clifford et al., 1997; Ibbotson et al., 1998). Borst and direction of subsequent motion (Levinson and Sekuler, Egelhaaf (1987) measured the time constant of the decay 1976; Patterson and Becker, 1996; Schrater and Simoncelli, of the response to impulsive (two-frame) motion in four 1998; Rauber and Treue, 1999; Alais and Blake, 1999). This conditions: control (no adaptation); preferred motion adap- phenomenon, known as the direction aftereffect (DAE), dif- tation; anti-preferred motion adaptation; and adaptation to fers from the classical MAE in that a moving test stimulus is counter-phase flicker. In all except the control condition used to measure the DAE. For angles up to around 100◦ be- they found that the decay time constant reduced to between tween the directions of motion of the adapting and test pat- 20 and 40% of its unadapted value, suggesting that adapta- terns, the perceived direction of the test pattern tends to be tion in that system, as for human speed perception, is driven repelled away from the adapting direction. The magnitude by temporal modulation (Fig. 17). of this repulsion can be as much as 40◦ for adapter-test an- In the NOT of the mammalian wallaby, preferred di- gles around 30◦. For larger obtuse angles between adapting rection motion causes the most significant adaptation. and test directions, the perceived direction of the test tends Anti-preferred motion or flicker induces some adaptation, to be attracted towards that of the adapter (Schrater and but it is far weaker than that induced by preferred direc- Simoncelli, 1998). The magnitude of this attraction effect tion motion (Clifford et al., 1997; Ibbotson et al., 1998). is smaller than that of the repulsion, peaking at around 15◦ However, the use of the time constant of the decay of the for angles of around 150–160◦ between adapter and test response to impulsive (two-frame) motion as an index of directions. adaptation has recently been criticized on the basis that The effect of motion adaptation on subsequent direction changes in the time constant as a function of adaptation discrimination depends upon the angle between the adapting can be modeled by the introduction of fixed high-pass tem- direction of motion and the baseline test direction around poral pre-filters prior to motion computation (Harris and which discriminations are made. For parallel adapting and O’Carroll, 2002). Thus, a caveat must be applied to the test directions, Phinney et al. (1997) found reductions in di- use of impulse response data to infer the determinants of rection discrimination thresholds of around 20% while Hol motion adaptation as in the wallaby experiments. Indeed, and Treue (2001) found little or no effect of adaptation. This Harris et al. (2000) have shown that adaptation to motion in discrepancy in the magnitude of the effect of adaptation on the HS neuron of the fly lobula plate is not simply driven by subsequent discrimination around the adapting direction re- the associated temporal modulation of contrast. This work mains mysterious. However, it is interesting to note that Hol is discussed in detail in Section 4.6. and Treue (2001) also report smaller effects of adaptation on In humans, prolonged exposure to a moving pattern af- motion detection than did Raymond (1993a), suggesting that fects not only the perceived speed but also the perceived their adaptation paradigm might somehow be less powerful 430 C.W.G. Clifford, M.R. Ibbotson / Progress in Neurobiology 68 (2003) 409–437 than those used in other studies. For angular differences of Stone and Thompson, 1992; Muller and Greenlee, 1994; 10–40◦ between adapting and baseline test directions, adap- Thompson et al., 1996) and, at low contrasts, speed dis- tation impairs subsequent discrimination performance, with crimination thresholds (Muller and Greenlee, 1994). How maximum threshold elevations of around 60% at adapt-test can we be sure that the effect of motion adaptation can- angular differences of 20–30◦ (Phinney et al., 1997; Hol and not be accounted for in terms of the fading of perceived Treue, 2001). contrast accompanying adaptation? Firstly, perceived speed Thus, for the direction as well as the speed of motion, has been shown to decrease with grating adaptation even there is psychophysical evidence that adaptation improves when contrast fading has been controlled for, such that test differential sensitivity to motion around the adapting level and reference gratings have perceptually matched contrasts at the expense of the introduction of perceptual biases. In (Clifford, 1997; Bex et al., 1999), and to decrease at a slower the case of speed, the perception of the actual adapting rate than perceived contrast (Clifford, 1997; Hammett et al., stimulus is affected, while it is only the perception of di- 1994). Secondly, while stimulus contrast has been found to rections of motion other than the adapting direction that is affect speed discrimination (Muller and Greenlee, 1994), perturbed. thresholds reach a lower asymptote by 10% contrast. In the range where contrast does affect speed discrimination, 4.6. Distinguishing motion adaptation from contrast thresholds increase with decreasing contrast. Thus, while adaptation response gain control at the level of V1 might underlie adaptation both to contrast and to temporal modulation, the The response of motion-sensitive cells in the primary effects of adaptation on perceived speed are not predictable visual cortex (V1) is modulated not only by stimulus tem- simply on the basis of contrast fading. poral frequency but also by contrast (Sclar et al., 1990). In the wide-field motion-sensitive HS neuron of the Indeed, it seems likely that temporal frequency adaptation fly lobula plate, Harris et al. (1999) have described three and contrast adaptation share V1 as a neural substrate. Nu- changes in the contrast response function induced by mo- merous studies have shown that speed and contrast are not tion adaptation. These are an after potential, a contrast gain independently coded (e.g. Muller and Greenlee, 1998), and reduction and a reduction in the cell’s output range. Plot- that adaptation decreases perceived contrast (Blakemore ting neuronal response as a function of the logarithm of et al., 1973; Georgeson, 1985; Hammett et al., 1994). Stim- contrast, these changes have the effect of shifting the re- ulus contrast has in turn been shown to affect perceived sponse function down, shifting it to the right and reducing speed over a wide range of contrasts (Thompson, 1982; its maximum, respectively (Fig. 18).

Fig. 18. The effects of adaptation on the contrast response function of the insect HS neuron (Harris et al., 2000). (A) Schematic of characteristic sigmoidal neuronal response as a function of log contrast. Adaptation induces: (B) an after-potential that shifts the contrast response function vertically; (C) contrast gain reduction that shifts the curve to the right; and (D) output range reduction that shifts the function to the right and reduces its maximum. C.W.G. Clifford, M.R. Ibbotson / Progress in Neurobiology 68 (2003) 409–437 431

The after potential appears to be dependent upon the motion detection will undoubtedly benefit from a continued activity of the cell during adaptation (Harris et al., 1999) comparative approach. and is probably associated with dendritic calcium accumu- In this review, we have emphasized the value of linking lation (Kurtz et al., 2000). The greater the depolarization theoretical and experimental approaches to motion detec- induced during adaptation, the greater the hyperpolarizing tion. We have described the basic structures of a range of after potential and the greater the downward shift of the theoretical motion detectors that have been designed since contrast response function. Adaptation to flicker or motion Exner (1894) sketched the first model. In recent years there orthogonal to the preferred direction induces only a weak has been a flurry of “new” models in the literature but we hyperpolarizing after potential. Anti-preferred motion hy- have tried to categorize them into a small number of mech- perpolarizes the cell during adaptation, inducing a weak anisms that use closely related concepts. We have then re- depolarizing after potential that shifts the contrast response viewed the evidence that these mechanisms are in operation function slightly upwards. Thus, the vertical shift of the at the cellular level in biological systems. “Model-chasing” contrast response function through adaptation shows a high has proved very popular with physiologists, pharmacologists degree of direction-selectivity. and anatomists in a range of species ranging from insects While the after potential is highly direction-selective, to primates. Some of the motion detector models appear to adapting motion in any direction reduces contrast gain by a be implemented, at least in part, in the visual systems of a similar amount. Flicker induces a much smaller reduction range of animal species. However, evolution appears to have in gain, suggesting that contrast gain reduction is dependent solved several issues in unique ways that have only become on a mechanism that is both motion-dependent and direc- evident by opening the cockpit of the relevant species. tion insensitive. The fact that flicker reduces contrast gain This review also emphasizes the importance of adapta- only weakly suggests that contrast gain control is driven by tion in the motion processing system. Psychophysicists have a signal obtained after the motion opponent stage that serves studied motion aftereffects, which are generated by adap- to attenuate flicker responses. To account for the direction tive mechanisms, for many years and yet it is only recently insensitivity of contrast gain control, this signal would have that science has really attempted to explain the phenomenon. to involve the pooled responses of motion detectors tuned to Given that motion detection is a dynamic process, we feel different directions. However, the reduction in contrast gain that it is important to consider motion adaptation as an in- appears to be retinotopic, such that the rightward shift in the tegral part of the motion detector process rather than as a contrast response function of the wide-field HS cell is only separate mechanism. With this in mind, the review has at- observed when adapting and test stimuli are presented in the tempted to link adaptation to the cellular and theoretical same location. This retinotopy suggests that gain reduction mechanisms of motion detection. We anticipate that treating must be occurring either in retinotopic elements presynaptic adaptation as a fundamental property of a complex system to HS or locally on its dendrites. Until more is known about of filters will continue to advance our understanding of the the anatomy and physiology of the visual pathway afferent dynamic aspects of vision in general and of motion detec- to the lobula plate (see Section 3.4), the mechanisms of tion in particular. contrast gain control are likely to remain unclear. A marked reduction in output range is only observed after adaptation to preferred or anti-preferred motion, not after References adaptation to orthogonal motion or flicker. This suggests that range reduction is activity dependent, being induced by Adelson, E.H., Bergen, J.R., 1985. Spatio-temporal energy models for the perception of motion. J. Opt. Soc. Am. A 2, 284–299. adapting stimuli that cause either a sustained depolarization Adelson, E.H., Bergen, J.R., 1986. 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