Ординатура / Офтальмология / Английские материалы / The Neuropsychology of Vision_Fahle, Greenlee_2003

VISION, BEHAVIOUR, AND THE SINGLE NEURON 13

brain at any given time, as is evident, e.g. in the neglect syndrome (see Chapter 7, this volume).

More evidence that neural activity does not simply reflect sensory inputs came from studies of binocular rivalry (Logothetis and Schall 1989; Leopold and Logothetis 1996; Sheinberg and Logothetis 1997). In these studies, a different visual stimulus is presented to each eye. Under these conditions, the viewer experiences perceptual alternations such that, typically, one or the other visual stimulus is dominant and ‘seen’, but rarely both together. Logothetis and co-workers found that activity in high-level ventral stream areas reflected the dominant percept, such that a neuron selective for the visual stimulus to the left eye would fire only when that visual stimulus was perceived, but not when the other stimulus was perceived even though sensory stimulation was constant. In early visual areas, on the other hand, only a small proportion of neurons changed activity when the perceptually dominant stimulus changed, with the majority of cells responding to the sensory characteristics of the stimuli.

Dorsal stream

The relation between neural and behavioural performance measures was further explored in awake monkeys by William Newsome and co-workers in the middle temporal (MT), the middle superior temporal (MST), and other cortical areas (Newsome et al. 1989; Britten et al. 1992). Of neurons in cortical area MT, 90% are motion-selective, responding optimally to stimulus motion in their preferred, but not in the opposite direction. Newsome and colleagues employed a random-dot stimulus that consisted of a large number of moving dots presented in the receptive field of the neuron under study. The coherence of the dots could be varied such that at 100% coherence all dots are moving in one direction (usually the preferred direction of the neuron under study), or else each dot moves in a random direction (0% coherence). Parametric variation of the coherence level allows simultaneous assessment of both neural and behavioural performance. The performance of a single neuron in this coherent motion task is depicted in Fig. 1.5.

Interestingly, Newsome and colleagues found that, under these conditions, the sensitivity of most MT neurons was very similar to the psychophysical sensitivity of the monkeys. In fact, there was often good quantitative agreement between parameters describing neuronal activity and behaviour as a function of stimulus coherence. This raised the possibility that psychophysical performance could be supported by relatively few statistically independent neurons, although larger ensembles would be required for partially intercorrelated neurons (Parker and Newsome 1998; Shadlen et al. 1996).

The parietal cortex contains several different areas that are thought to play a role in the coordinate transformations that are required to perform visually guided movements. The location where an object forms an image on the retina will depend on where the eyes happen to be looking at a given moment. To accurately reach for the object, eye position relative to the head as well as head position relative to the body have to be taken into account since a large variety of possible head and eye positions will require the same arm movement. Accordingly, parietal areas that are thought to be

(a)

.8%

Fig. 1.5 Processing of coherent motion in the middle temporal (MT) area. (a) Response histograms for a single MT neuron as a function coherence of the moving dot pattern. The abscissa shows the neural firing rate obtained on a given trial, and the ordinate shows how often each value occurred. The dark bars represent the non-preferred or null direction, and the open bars represent the preferred direction. The depth axis represents different coherence levels. Each distribution is based on 60 trials (2 s duration each). (b) ROC curves comparing neural responses for the preferred to the null direction at different coherence levels. With increasing coherence level the separation between preferred and null direction increases and this leads to the ROC curve being further away from the diagonal, (c) This neurometric curve is generated by plotting the area under the ROC curve for each coherence level against stimulus coherence. It provides a measure of neural performance in motion discrimination that can be easily compared with behavioural performance. (Modified from Britten et al. (1992).)

VISION, BEHAVIOUR, AND THE SINGLE NEURON 15

involved in these transformations receive visual, somatosensory, and vestibular projections as well as efference copies of motor commands. One important concept for parietal cortex function is the gain field, which was described by Andersen and co-workers in parietal areas 7a and the lateral interparietal (LIP) cortex (Andersen and Mountcastle 1983; Andersen et al. 1985). Convergent eye position and visual signals produce visual neural responses in these areas that have retinocentric receptive fields and are modulated in a monotonic fashion by the orbital position of the eye. The term ‘gain field’ refers to the fact that eye position appeared to modify the gain of the visual responses. As Andersen and colleagues noted, these gain fields represent a stage in the coordinate transformation required to perform accurate reaching movements. Importantly, the activity of a single neuron does not unambiguously communicate information about stimulus and eye position—different combinations of retinocentric stimulus position and gain can lead to the same neural activity. Only across a population of neurons will the ensemble activity be unique for each configuration. Consistent with a general involvement in action, Michael Goldberg and co-workers have found evidence that parietal cortex preferentially processes visual stimuli that appear relevant for behaviour, regardless of whether an eye movement is actually carried out or not. For example, in a recent experiment conducted in the LIP (Gottlieb et al. 1998), monkeys were trained to make eye movements that brought a stimulus into the receptive field of the neuron under study. Gottlieb et al. found that the response of the neuron varied dramatically depending on whether the stimulus was salient for the monkey or not. If the stimulus had only recently appeared and was thus interesting or salient for the monkey, an LIP neuron gave a large response when the stimulus entered its receptive field after the saccade. These neurons showed little or no response when the same stimulus had been part of the display for a while and was thus nonsalient when it entered the neurons’ receptive field. These two studies and a large body of additional work have begun to elucidate how visual information is processed and used to guide movements in parietal cortex.

Another area that plays a major role in sensorimotor transformations is the premotor cortex, where Giacomo Rizzolatti and collaborators have made important contributions. Premotor neurons show selective responses during tasks in which monkeys see and subsequently grasp objects (Murata et al. 1997). An example of such a neuron is shown in Fig. 1.6.

Because these responses occur both when monkeys are preparing to grasp known objects in complete darkness and also when they observe objects but never make subsequent grasping movements, they cannot be understood as exclusively visual or motor-related. Instead, the premotor cortex represents objects in terms of their pragmatic or motor-related properties, such as potential grasping movements that could be made towards them. This interpretation is consistent with mirror neurons, which Rizzolatti and colleagues (1996) have described in premotor cortex. These neurons are active both when a monkey performs a grasping movement himself, and also when the

16 GREGOR RAINER AND NIKOS K. LOGOTHETIS

10 spikes/s

Fig. 1.6 Grasping neuron in premotor cortex. The response of a single neuron during the grasping of each of six objects is shown in raster format. Dark vertical bars represents action potentials, with subsequent trials shown in different rows. Activity histograms are shown for each object under the corresponding rasters. The trial began with the onset of a red light (a), which was the signal for the monkey to press a key (b). Upon pressing the key, an object was presented to the monkey for 1 second. Then, the onset of a green light (c) cued the monkey to release the key (d) and grasp the object, which occurred at time (e). The onset of a second green light (f) cued the monkey to release the object, which occurred at time (g). This premotor neuron thus showed robust activity during both observation and grasping of a ring. (After Murata et al. (1997).)

monkey observes a human while he makes a similar grasping movement. Further light on how premotor cortex represents visual attributes came from Michael Graziano and collaborators (1994). They demonstrated that the premotor representation of visual space is in many cases anchored to the face or to the body parts. That is, a neuron might represent the visual space right next to the monkey’s arm, and would thus respond whenever a visual stimulus appeared near that arm, largely independent of the arm’s actual position. Neurons of this kind were in general also selective for somatosensory stimulation, responding to touch of a body region near the visually modulated area. Such an arm-centred representation of space that combines both visual and somatosensory information could be very useful for goal-directed movement (Graziano and Gross 1998), which is thought to be a major function of the dorsal visual pathway.

VISION, BEHAVIOUR, AND THE SINGLE NEURON 17

Frontal cortex

Major progress was made in our understanding of the frontal cortex when Joaquin Fuster and Garrett Alexander (1971) discovered delay activity. They found that, during periods in which monkeys had to maintain the location of an item in short-term or working memory, neurons in the prefrontal cortex exhibited periods of sustained activity that could last for the entire duration of the delay. This delay activity is thought to be the neural correlate of active maintenance of information in memory. The working memory functions of the prefrontal cortex were further explored extensively by Patricia Goldman-Rakic and co-workers (Funahashi et al. 1989). They employed an oculomotor delayed-response task in which a monkey was briefly cued with a spot of light at a peripheral location. After a delay, the monkey had to execute an eye movement to the remembered location of the cue. Funahashi et al. found that delay activity of prefrontal neurons varied as a function of the location of the cue. An example is shown in Fig. 1.7.

Delay activity was found to be specific to the location of a particular cue, but does it reflect visual memory for the spot of light or movement preparation for the forthcoming saccade? In an experiment that allowed the dissociation between sensoryand motor-related activity, Funahashi and colleagues (1993) demonstrated that, in general, prefrontal neurons reflected both these processes but the majority coded the memory of the cue. However, prefrontal neurons not only support memory for spatial locations but also subserve working memory for objects (Rao et al. 1997; Rainer et al. 1998), hence lesions in these regions should interfere with spatial abilities. But does cue-specific memory actually reflect active maintenance or simply an automatic perseverance of sensory events? This question was addressed by Earl Miller and co-workers (1996), who employed a paradigm investigating working memory for objects. Miller and colleagues showed that delay activity for objects survived intervening stimuli in prefrontal, but not IT cortex. They employed a delayed-matching-to-sample task in which a cue object needed to be remembered and compared to up to five possible distractors presented sequentially and separated by delays. In the delays following the distractor objects, IT neurons tended to reflect the characteristics of these distractors, whereas neurons in prefrontal cortex maintained a memory of the initial cue object across the intervening stimuli, consistent with a role in the active short-term maintenance of information. This work demonstrates an important difference between prefrontal and IT object selectivity, and raises the question whether neurons in these areas exhibit other differences.

Recent work has addressed this question, and demonstrated that prefrontal object selectivity is tightly coupled to a monkey’s behavioural performance (Rainer and Miller 2000). Using objects degraded with noise, Rainer and Miller showed that monkeys were better able to identify familiar objects in the presence of noise as compared to novel objects. This experience-dependent improvement was reflected in prefrontal neurons, in that their object selectivity was more robust to degradation for familiar than for novel objects. This suggests that prefrontal neurons have activity patterns consistent with

18 GREGOR RAINER AND NIKOS K. LOGOTHETIS

5111.1-4

50 spikes/s

Fig. 1.7 Working memory neuron in prefrontal cortex. The response of a single neuron during the performance of an oculomotor delayed-response task is shown. A peripheral cue is briefly presented at one of eight locations located at 13 eccentricity (see centre panel), corresponding to the period marked (C) on each of the histograms. This is followed by a delay (D) and a response (R) period, in which the monkey had to maintain central fixation and make a saccadic eye movement to the remembered location of the cue, respectively. Above each histogram the action potentials for this neuron on several trials can be seen, with each vertical tick representing a single action potential. This single neuron showed robust activity whenever the monkey needed to remember a cue presented on the upper right (135 ), with activity falling off rapidly towards the other locations. This delay activity is thought to be the neural correlate of working memory. (After Funahashi et al. (1989).)

a role in supporting behavioural performance, and that this selectivity can be strongly modulated by experience. Such changes are consistent with work by Jeff Schall and co-workers (Bichot et al. 1996) in the frontal eye field (FEF). Single neurons in the FEF are generally not object-selective but, after extensive training on a visual search task with coloured targets, Bichot and colleagues found that FEF neurons exhibited color selectivity. These and other studies implicate the prefrontal cortex in short-term memory and, more generally, in the processing of currently relevant stimuli.

VISION, BEHAVIOUR, AND THE SINGLE NEURON 19

Conclusions

The studies outlined in this chapter represent only a small sample of the wealth of information that has been accumulated by recording the activity of single neurons in monkeys. Yet it demonstrates that significant progress has been made, not only in our understanding of the representation of sensory stimuli, but also in uncovering neural correlates of cognitive operations such as attention, decision-making, or associative memory formation. Perhaps the most significant progress has been made by parametric studies, because they allow accurate quantification of effects and permit the most meaningful correlations with behavioural performance. Several new techniques for studying the brain have been developed recently. For example, functional magnetic resonance imaging (fMRI) allows the study of average activity levels across a large number of neurons (see Chapter 4, this volume), whereas two-photon microscopy permits the in vivo imaging of individual neurons and even synapses. In addition, in vitro physiology is continuing to provide a wealth of information about how activity can modulate synaptic efficacy by means of mechanisms such as long-term-potentiation (LTP) or depotentiation (LTD). The challenge for monkey electrophysiology will be to integrate knowledge from these and other techniques to uncover the mechanisms by which single neurons process information in a behaving cognitive organism, how neurons underlie our conscious experience, and how progress in understanding can be used to combat neurological disorder and disease.

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