Spatial determinants of multisensory integration in cat superior colliculus neurons.

The superior colliculus (SC) plays an important role in integrating visual, auditory and somatosensory information, and in guiding the orientation of the eyes, ears and head. Previously we have shown that cats with unilateral SC lesions showed a preferential loss of multisensory orientation behaviors for stimuli contralateral to the lesion. Surprisingly, this behavioral loss was seen even under circumstances where the SC lesion was far from complete. To assess the physiological changes induced by these lesions, we employed single unit electrophysiological methods to record from individual neurons in both the intact and damaged SC following behavioral testing in two animals. In the damaged SC of these animals, multisensory neurons were preferentially reduced in incidence, comprising less than 25% of the sensory-responsive population (as compared with 49% on the control side). In those multisensory neurons that remained following the lesion, receptive fields were nearly twofold larger, and less than 25% showed normal patterns of multisensory integration, with those that did being found in areas outside of the lesion. These results strongly suggest that the multisensory behavioral deficits seen following SC lesions are the combined result of a loss of multisensory neurons and a loss of multisensory integration in those neurons that remain.

The present studies were initiated to explore the basis for the response suppression that occurs in cat superior colliculus (SC) neurons when two spatially disparate stimuli are presented simultaneously or in close temporal proximity to one another. Of specific interest was examining the possibility that suppressive regions border the receptive fields (RFs) of unimodal and multisensory SC neurons and, when activated, degrade the neuron's responses to excitatory stimuli. Both within- and cross-modality effects were examined. An example of the former is when a response to a visual stimulus within its RF is suppressed by a second visual stimulus outside the RF. An example of the latter is when the response to a visual stimulus within the visual RF is suppressed when a stimulus from a different modality (e. g., auditory) is presented outside its (i.e., auditory) RF. Suppressive regions were found bordering visual, auditory, and somatosensory RFs. Despite significant modality-specific differences in the incidence and effectiveness of these regions, they were generally quite potent regardless of the modality. In the vast majority (85%) of cases, responses to the excitatory stimulus were degraded by >/=50% by simultaneously stimulating the suppressive region. Contrary to expectations and previous speculations, the effects of activating these suppressive regions often were quite specific. Thus powerful within-modality suppression could be demonstrated in many multisensory neurons in which cross-modality suppression could not be generated. However, the converse was not true. If an extra-RF stimulus inhibited center responses to stimuli of a different modality, it also would suppress center responses to stimuli of its own modality. Thus when cross-modality suppression was demonstrated, it was always accompanied by within-modality suppression. These observations suggest that separate mechanisms underlie within- and cross-modality suppression in the SC. Because some modality-specific tectopetal structures contain neurons with suppressive regions bordering their RFs, the within-modality suppression observed in the SC simply may reflect interactions taking place at the level of one input channel. However, the presence of modality-specific suppression at the level of one input channel would have no effect on the excitation initiated via another input channel. Given the modality-specificity of tectopetal inputs, it appears that cross-modality interactions require the convergence of two or more modality-specific inputs onto the same SC neuron and that the expression of these interactions depends on the internal circuitry of the SC. This allows a cross-modality suppressive signal to be nonspecific and to degrade any and all of the neuron's excitatory inputs.

Visual response properties were examined in the superficial layers of the superior colliculus (SC) of anesthetized, paralyzed cats before and after i.v. administration of d-amphetamine. Receptive fields (RFs) of single SC units were plotted using small spots of light presented to the contralateral eye. Within the first hour following d-amphetamine injections, RF size gradually increased, reaching a maximum 86 min post-injection. On average, the area of the RF increased by 5.6 times and RF expansion was observed in all single units examined in the superficial layers. Over the subsequent 4-8 h following the injection, RF area gradually decreased and returned to control dimensions. Most RFs displayed asymmetrical patterns of expansion, showing relatively more horizontal than vertical growth. As RF expansion developed, responses to stimuli flashed "on" and "off" at various locations both inside and outside the borders of the control RF became progressively more vigorous. In contrast, no significant changes were noted in direction-selective responses at any time after d-amphetamine injections. Using an array of light bar stimuli of different lengths, the strength of surround suppression was found to be significantly diminished by d-amphetamine. The reduction in surround suppression was especially clear for bar lengths which exceeded the diameter of the control RF. No RF expansion was observed in the superficial layers of the SC when d-amphetamine was injected intravitreally. Furthermore, d-amphetamine had no discernable effect on the RF sizes of cells in the visual cortex. These results suggest that the RF changes in the SC were not of either retinal or cortical origin. We conclude that the mean retinal area which can potentially influence the activity of RFs in the superficial layers of the SC may be on average over 5 times greater than the RF area determined using conventional methods and criteria. These findings raise the interesting possibility that the relatively small size and sharp borders characteristic of RFs in the superficial layers arise from local inhibitory networks which delimit a broader field of excitatory activity supplied by retinal and cortical afferent terminals. Thus, in order to generate the RF changes observed here, either these local inhibitory circuits are amphetamine sensitive, or more likely, these inhibitory networks are dynamically modulated by an, as yet unidentified, amphetamine-sensitive input affecting visual RFs in the superficial layers.

Superior colliculus (SC) neurons have the ability to synthesize information from different sensory modalities, resulting in enhancements (or depressions) of their activity. This physiological capacity is, in turn, closely tied to changes in overt attentive and orientation responses. The present study shows that, in contrast to more altricial species, many deep layer SC neurons in the rhesus monkey are multisensory at birth. Such neurons can respond to stimuli from different sensory modalities, and all convergence patterns seen in the adult are represented. Nevertheless, these neurons cannot yet synthesize their multisensory inputs. Rather, they respond to combinations of cross-modal stimuli much like they respond to their individual modality-specific components. This immature property of multisensory neurons is in contrast to many of the surprisingly sophisticated modality-specific response properties of these neurons and of their modality-specific neighbors. Thus, although deep SC neurons in the newborn have longer latencies and larger receptive fields than their adult counterparts, they are already highly active and are distributed in the typical adult admixture of visual, auditory, somatosensory, and multisensory neurons. Furthermore, the receptive fields of these neurons are already ordered into well organized topographic maps, and the different receptive fields of the same multisensory neurons show a good degree of cross-modal spatial register. These data, coupled with those from cat, suggest that the capacity to synthesize multisensory information does not simply appear in SC neurons at a prescribed maturational stage but rather develops only after substantial experience with cross-modal cues.

The visual representation in the uncrossed retinal projection to the superior colliculus (SC) was examined electrophysiologically by recording multi-unit responses in paralysed, anaesthetised adult rats (both pigmented and albino), which had been monocularly enucleated either prenatally or soon after birth. This manipulation partially stabilises an exuberant neonatal projection from the remaining eye to the ipsilateral SC. Neuronal responses were also stronger and the multi-unit receptive fields larger than in intact animals. Many of the visual fields recorded on penetrations in caudal SC were located in the peripheral ipsilateral visual hemifield, corresponding to nasal retina. Such receptive fields are not seen in normal animals and were not found in animals enucleated on day 3 or later. The topographic representation of the dorso-ventral retinal axis, lateral to medial in the SC, was normal in all experimental animals. The representation of the naso-temporal retinal axis was abnormal and more variable. In all operated animals as the recording electrode was moved caudally away from the rostral pole of the SC, the corresponding receptive fields moved gradually from up to 40 degrees in the ipsilateral visual hemifield to about 40 degrees into the contralateral hemifield (a location corresponding to the peripheral edge of the temporal retina). This is the mapping polarity found in the normal uncrossed retinal projection. In the enucleated animals, the map was expanded and frequently displayed a clustering of fields arising from far temporal retina. In animals enucleated prenatally or on the day of birth, visual responses could be recorded in more caudal SC. The corresponding receptive fields now moved nasally on the retina, generating reversals in the map. The most caudal penetrations in these early enucleates frequently gave receptive fields located in retina nasal to the optic disc, up to 90 degrees into the ipsilateral visual hemifield. These results demonstrate that a temporal relationship exists between the order and mapping polarity of the visual field in SC and the time of enucleation. Prenatal enucleation produces reversals of the mapping polarity in caudal SC while neonatal enucleation produces an expanded map but one with a mapping polarity appropriate for an uncrossed projection.

Visual neuroscientists seek to answer two related questions. First, what does the visual system do? Second, how does it do it? While an answer to the second question is a description based on anatomy and biophysics, an answer to the first question is a description of computations performed on images. Important steps in providing this description were made with the publication of two classical studies on primary visual cortex by Movshon et al. (1978a,b). These studies have not aged in their readability, and do not require an interpretive key to be enjoyed by the contemporary reader. As an introduction, however, this Perspectives article briefly reviews the state of the art at the time of their appearance, their main findings, and their influence on three decades of subsequent investigations. The 1960s brought important advances in the understanding of the computations performed by the retina. An influential view emerged that described retinal ganglion cells as linear filters, i.e. as processes that compute a weighted sum of the intensities in the stimulus, with weights given by the receptive field (Enroth-Cugell & Robson, 1966). This simple 'linear model' applied to the majority of ganglion cells in the cat retina (those of the X type, corresponding to P cells in primates). It was quite powerful, as it promised to predict the responses of X ganglion cells to arbitrary stimuli, on the basis of the simple knowledge of the receptive field. This purely linear view was complemented by the discovery that another kind of ganglion cells, those of Y type (which correspond to M cells in primates), perform non-linear operations, suggesting that they sum the distorted output of subunits that in turn have linear receptive fields (Enroth-Cugell & Robson, 1966; Hochstein & Shapley, 1976). This arrangement confers position invariance: for stimuli of high spatial frequency, Y cells respond equally regardless of position within the receptive field. These advances suggested new ways of looking at simple and complex cells in primary visual cortex. Hubel & Wiesel (1962) had defined simple cells as having distinct antagonistic regions in their receptive fields, and had suggested that knowing those regions, one could predict 'the responses to any shape of stimulus, stationary or moving'. They had defined complex cells as any cell that was not simple, and had reported that complex cells achieved position invariance within their receptive field: they would respond to a stimulus of the appropriate orientation regardless of position within the receptive field. These attributes of simple and complex cells resembled those of X and Y ganglion cells. Some authors proposed that the correspondence could be anatomical, i.e. that it reflected predominance of X inputs to simple cells and of Y inputs to complex cells (Stone, 1972; Movshon, 1975), a suggestion that was not later confirmed. More generally, however, the linear and non-linear models and the stimulation procedures that had been so useful to analyse X and Y cells (Enroth-Cugell & Robson, 1966; Hochstein & Shapley, 1976) constituted promising starting points for a concise and precise characterization of simple and complex cells. In addition to the breakthroughs in retinal physiology, another force was pushing towards the use of quantitative engineering techniques in primary visual cortex: such techniques were proving successful to study human perception. In particular, the University of Cambridge – where Movshon, Thompson and Tolhurst operated – was a hotbed of research into the relations between single neuron responses and perceptual phenomena. These phenomena were investigated with rigorous psychophysical measures, and described with quantitative models based on image filtering (Graham, 1989; Wandell, 1995). Much of this research rested on the concept of 'channels', which are linear filters. Simple cells in the cortex seemed to be good candidates for such a role. Did they exhibit linear summation? The time had come to apply the power of linear systems analysis and related techniques to primary visual cortex. In their first article, Movshon et al. (1978a) applied linear systems analysis to the responses of simple cells. They measured responses of simple cells to gratings and bars, and asked if such responses were consistent with the output of a linear receptive field (Fig. 1A). The models of simple and complex cells proposed by Movshon, Thompson and Tolhurst ( Movshon et al. 1978a,b) A, linear model of simple cells. The first stage is linear filtering, i. e. aweighted sum of the image intensities, with weights given by the receptive field. The second stage isrectification: only the part of the responses that is larger than a threshold is seen in the firing rate response. B, subunit model of complex cells. The first stage is linear filtering by a number of receptive fields such asthose of simple cells (here we show four of them with spatial phases offset by 90 deg). The subsequent stages involve rectification, and then summation. Much as Enroth-Cugell & Robson (1966) had done for X retinal cells, they asked if the responses to drifting sinusoidal stimuli were sinusoidal, as one would expect from a linear filter. The results supported this view, provided that the responses were rectified by the spike threshold, which shows only the part of the responses that lie above threshold (Fig. 1A). The authors then asked how the responses depended on the spatial phase of a standing grating whose contrast oscillated sinusoidally in time. This test had been applied to X and Y retinal cells by Hochstein & Shapley (1976). Here the linear model was put to a quantitative test, and the fit was good, provided again that the output of the receptive field was passed through a rectification stage that thresholded it (Fig. 1A). Movshon, Thompson and Tolhurst were even able to suggest how high the threshold should be relative to rest. They expressed this threshold in the units of firing rate responses, spikes s−1. For example, for the cell in their Fig. 4, the estimated threshold was 8 spikes s−1 (if the receptive field were to output 12 spikes s−1, the neuron would output 4 spikes s−1). Finally, the authors asked a key question: was the selectivity of simple cells predictable on the basis of the receptive field alone, as had been suggested by Hubel and Wiesel, and as would be expected of a linear filter? To test this hypothesis, they turned again to the approach that had demonstrated the linearity of X retinal cells (Enroth-Cugell & Robson, 1966). First, they measured responses to drifting gratings of various spatial frequencies (Fig. 2A). Then, they measured responses to bars flashed at various positions, thus estimating the profile of the receptive field (Fig. 2B, histograms). According to the linear hypothesis, the first data set could be used to predict the second one. This was indeed the case for the cells (Fig. 2B, curve): just as predicted by the linear model, the selectivity of simple cells for spatial frequency could be predicted on the basis of the receptive field profile. Linearity of spatial summation in simple cells the experiment in Fig. 9 of the first 1978 article by Movshon, Thompson and Tolhurst ( Movshon et al. 1978a) Responses were simulated from a model simple cell with a linear spatial receptive field that summates strongly distorted thalamic inputs (Carandini et al. 2002). A, spatial frequency tuning of the simple cell. The ordinate marks the amplitude of the sinusoidal modulation caused by drifting sinusoidal gratings, whose spatial frequency is plotted on the abscissa. B, profile of the receptive field of the simple cell. The histogram shows the firing rate elicited by flashing bars in various spatial positions across the receptive field. Negative responses indicate responses elicited when withdrawing the bar. The curve shows the prediction based on linearity, obtained by Fourier transform of the data in A. For all its success in explaining responses of simple cells, the linear model (Fig. 1A) could not possibly work for complex cells. Complex cells are insensitive to the precise position of a bar within the receptive field, and respond both to the onset and to the offset of the bar. Neither of these properties could arise from a single linear receptive field. Hubel & Wiesel (1962) had therefore described complex cells as summing the output of a number of simple cells with similar orientation preference but different receptive field profiles. Borrowing from the subunit model proposed for Y ganglion cells by Hochstein & Shapley (1976), such a description could be made quantitative by postulating a number of linear receptive fields orientated in space (subunits), whose outputs are rectified by threshold, and integrated into a single response (Fig. 1B). In their second article, Movshon et al. (1978b) went on to propose such a subunit model for complex cells (Fig. 1B), and to justify each of its components. They started by performing the same three measures that they had performed in simple cells. First, the authors studied the modulation in firing rate caused by drifting sinusoidal gratings. Complex cells responded phasically only at the lowest spatial frequencies, but as soon as the frequency approached the optimal, the responses became constant in time. This was consistent with a subunit model (Fig. 1B). Each of the subunits would respond with a sinusoid, but the rectified sinusoids would be offset in time, and therefore they would sum to an approximately constant value. Second, they asked how complex cell responses depended on the spatial phase of a standing grating whose contrast reversed in time. Just as for Y ganglion cells (Hochstein & Shapley, 1976), the cells did not care for spatial phase, giving two responses for each cycle of the stimulus (once for each sign of contrast). These results again were consistent with the subunit model (Fig. 1B): each subunit would give positive responses only once in each cycle, and only at specific spatial phases, but the sum of the positive subunit responses would rise twice in each cycle, irrespective of spatial phase. Third, they asked whether the selectivity of complex cells was predictable on the basis of the receptive field alone. The answer was a resounding no: as predicted by the subunit model (Fig. 1B), receptive fields of complex cells don't have strong distinct subregions, so it is not possible to predict their selectivity from a receptive field profile. While all these results seemed consistent with a subunit model (Fig. 1B), a key attribute of that model remained to be verified: are the receptive fields of the subunits really linear? To test this hypothesis, the authors devised an elegant experiment, in which they measured the interaction of dark and light bars in different positions of the receptive field. To understand this experiment, consider a simplified model of complex cell that includes only four subunits (Fig. 1B), and imagine placing a bright bar over the central positive region in the receptive field of the first subunit. This subunit will give a strong positive response, and will thus operate well above threshold. Conversely, the third subunit, which has the opposite receptive field, will be strongly suppressed, and will operate much below threshold. The second and fourth subunits, in turn, will barely respond, so they will operate (as normally) slightly below threshold. Adding a second bar to the stimulus therefore will mostly reveal the receptive field properties of the first subunit. Indeed, as shown in Fig. 8 of the original article, the interaction profiles between bars resemble receptive fields. If these receptive fields of the subunits operate linearly (and if all subunits are similar in spatial frequency preferences), then it should be possible to predict the spatial frequency selectivity of the neuron based on the receptive field profile of the subunits, just as for simple cells (Fig. 2). The results of this experiment, illustrated in Fig. 9 of the original article, confirm this prediction, providing strong support for the subunit model of complex cells (Fig. 1B). The results of the two articles therefore can be summarized by two models: the linear model of simple cells (Fig. 1A), and the subunit model of complex cells (Fig. 1B). These models share two key attributes: (1) all the image processing is performed by linear filters; (2) the non-linearities operate on the time-varying signals that are output by the filters. These models have formed the basis for much that has followed in the subsequent three decades. There is obviously no space here to cover this territory, for which we refer the reader to recent reviews (e.g. Carandini et al. 2005). What might be more useful would be to discuss those aspects that, with 100% hindsight, could have been analysed differently, and would arguably have led to slightly different conclusions. First, when comparing responses to gratings to the profiles of the receptive field (Fig. 2), whether for simple cells or for the subunits of complex cells, a free scaling factor was allowed to obtain a match. Such a scaling factor should not be necessary for the simple models shown in Fig. 1, but it is necessary for actual cells, whose responsiveness depends very much on the local distribution of contrast. To account for this dependence, the models were later extended to include a divisive stage that controls responsiveness on the basis of the distribution of local contrast (for reviews see Heeger, 1992; Carandini et al. 1999). Second, the authors were perhaps wise to concentrate on one spatial dimension, and thus to avoid the contentious issue of orientation selectivity. Orientation tuning curves were not measured in this study, arguably because rotating the stimulation device involved placing one's hand within centimetres of electrocution, something that was done only reluctantly, once for each cell (J. A. Movshon, personal communication). Had the authors measured 2-dimensional receptive field profiles they could have asked whether these profiles predicted orientation selectivity. This issue remained open for decades and is not entirely closed to this date (reviewed by Ferster & Miller, 2000). Finally, perhaps the greatest limitation of these studies is that they concentrated on the spatial domain, and did not test linearity in the temporal domain. On the one hand, as was shown in the subsequent decades, the concept of spatial receptive field can be fruitfully extended to 3-dimensional space–time, to account for phenomena of direction selectivity (in fact, the nascent signs of such an extension can be seen already in the second 1978 article). On the other hand, as demonstrated later by Tolhurst et al. (1980), in primary visual cortex temporal summation is far from linear: responses are much more transient than would be expected from the frequency selectivity curves. This non-linearity is puzzling: how could a receptive field be spatially linear and temporally non-linear? It is now thought that because a cortical cell sums inputs from a variety of spatially displaced thalamic neurons, even if the individual inputs are grossly distorted by saturating and threshold non-linearities, the overall spatial summation properties of the neuron will remain approximately linear (Carandini et al. 2002; Priebe & Ferster, 2006). Indeed, the simulations shown here (Fig. 2) resulted from a model cell that summates such strongly non-linear inputs. This model cell passes the test of spatial linearity devised by the 1978 papers (Fig. 2), yet it would fail any test of temporal linearity. If it took three decades to obtain this realization, it is possibly because these 1978 papers made such a compelling case for linearity, and the field took them as evidence that every step in the visual system up to the primary visual cortex had to be linear. The reasoning, partly explicit in these papers and implicit in much of the subsequent literature, was that there could be no non-linear stages from the cones to the cortex because those stages would have prevented the cortical cells from passing the linearity tests. These are modest limitations, and they are evident only with three decades of hindsight. Overall the impact of these papers on the field was forceful and positive. As is evident from glancing at any recent review (e.g. Carandini et al. 2005) much of what was done to this date to explain responses of primary visual cortex, e.g. to explain properties such as orientation selectivity, direction selectivity and binocular integration, rested on the results of these two classical papers. Moreover, these studies succeeded in providing a foundation for the models of pattern perception based on psychophysical channels (Graham, 1989). Our only hope is that similarly powerful quantitative studies will soon appear for areas beyond the primary visual cortex.

A topographic map of auditory space is a feature found in the superior colliculus (SC) of many species, including CBA/CaJ mice. In this genetic background, high-frequency monaural spectral cues and interaural level differences (ILDs) are used to compute spatial receptive fields (RFs) that form a topographic map along the azimuth. Unfortunately, C57BL/6 mice, a strain widely used for transgenic manipulation, display age-related hearing loss (AHL) because of an inbred mutation in the Cadherin 23 gene (Cdh23) that affects hair cell mechanotransduction. To overcome this problem, researchers have used young C57BL/6 mice in their studies, as they have been shown to have normal hearing thresholds. However, important details of the auditory response characteristics of the SC such as spectral responses and spatial localization, have not been characterized in young C57BL/6 mice. Here, we show that two- to four-month C57BL/6 mice lack neurons with frontal auditory RFs and therefore lack a topographic representation of auditory space in the SC. Analysis of the spectrotemporal RFs (STRFs) of the SC auditory neurons shows that C57BL/6 mouse SC neurons lack the ability to detect the high-frequency (>40 kHz) spectral cues that are needed to compute frontal RFs. We also show that crossing C57BL/6 mice with CBA/CaJ mice or introducing one copy of the wild-type Cdh23 to C57BL/6 mice rescues the high-frequency hearing deficit and improves the topographic map of auditory space. Taken together, these results demonstrate the importance of high-frequency hearing in computing a topographic map of auditory space.

1. Experiments in anaesthetized cats have shown that the superior colliculus receives deep afferent input from the forelimb and hindlimb, but not from the large superficial neck muscles. 2. Neuronal activity in the superior colliculus is readily elicited by electrical stimulation of C2 and C3 cutaneous nerves. A significant proportion of neurones so activated have multiple receptive fields and some with no identifiable receptive fields in regions innervated by C2 and C3 nerves have receptive fields elsewhere on the body surface. Many collicular neurones activated by C2 and C3 stimulation had no identifiable receptive fields. 3. Natural stimuli to the limbs, hitherto believed to activate only cutaneous receptors, are sufficient to activate deep receptors which contribute to the neuronal responses in the superior colliculus elicited by the natural stimulus. These same natural stimuli set up transmitted vibration adequate to excite receptors some distance from the applied stimulus. 4. No evidence was found for a rigorous somatotopy in the superior colliculus. The great majority of neurones received trigeminal input which is widely distributed throughout the superior colliculus. 5. Tactile stimuli to the face are most effective in eliciting unit activity in the superior colliculus and many neurones activated by these stimuli were shown to be tectospinal neurones. In particular, the specialized receptors of the face, including the glabrous skin of the snout (the planum nasale) and the vibrissae, are major sources of input to collicular neurones including tectospinal neurones. 6. It is suggested that a major role of the superior colliculus is in the organization of head movements associated with the use of the specialized receptor organs of the face in exploratory behaviours. The superior colliculus may also be involved in the organization of aversion movements of the head.

When a tactile stimulus touches the body on one side, animals show an orienting response toward that side with the eyes, the head or the entire body. This movement requires the transformation of sensory information into motor commands. The superior colliculus is supposed to be a fundamental part of the brain where this sensorimotor transformation occurs and where one of the possible mechanisms could be the alignment among sensory and motor topographies. We changed the body shape of mice in order to analyse the development of new orienting responses following tactile stimulation. To do this, we bent the left vibrissae from left to right such that they were located in the right portion of the visual hemifield. If left-right inversion was performed in adults, tactile stimulation of the left vibrissae performed from the right produced wrong orienting movements to the left. Conversely, if left-right inversion was performed in newborns, mice learned to respond correctly to the right. By recording from superior colliculus multisensory neurons of mice whose vibrissae were displaced at birth, we found a shift of visual and auditory receptive fields from left to right in those multisensory neurons receiving tactile input from the displaced vibrissae. These results show the strict relation existing between the neuronal modifications in the superior colliculus and the changes in orienting behaviour. These findings also suggest two important conclusions. First, sensory mapping in the superior colliculus depends on sensory inputs coming from the same portion of space.(ABSTRACT TRUNCATED AT 250 WORDS)

The red-green axis of colour vision evolved recently in primate evolutionary history. Signals serving red-green colour vision travel together with signals serving spatial vision, in the parvocellular (PC) division of the subcortical visual pathway. However, the question of whether receptive fields of PC pathway cells are specialized to transmit red-green colour signals remains unresolved. We addressed this question in single-cell recordings from the lateral geniculate nucleus of anaesthetized marmosets. Marmosets show a high proportion of dichromatic (red-green colour-blind) individuals, allowing spatial and colour tuning properties of PC cells to be directly compared in dichromatic and trichromatic visual systems. We measured spatial frequency tuning for sine gratings that provided selective stimulation of individual photoreceptor types. We found that in trichromatic marmosets, the foveal visual field representation is dominated by red-green colour-selective PC cells. Colour selectivity of PC cells is reduced at greater eccentricities, but cone inputs to centre and surround are biased to create more selectivity than predicted by a purely 'random wiring' model. Thus, one-to-one connections in the fovea are sufficient, but not necessary, to create colour-selective responses. The distribution of spatial tuning properties for achromatic stimuli shows almost complete overlap between PC cells recorded in dichromatic and trichromatic marmosets. These data indicate that transmission of red-green colour signals has been enabled by centre-surround receptive fields of PC cells, and has not altered the capacity of PC cells to serve high-acuity vision at high stimulus contrast.

Although the direction selective properties of the superficial layer cells of the cat's superior colliculus have been extensively studied, the mechanisms underlying this property remain controversial. With the aim to understand the mechanism(s) underlying directional selectivity of collicular neurons we examined the substructure of their visual receptive fields. 1. The strength of cell responses and the direction selectivity indices varied in relation to the location of the tested region within the receptive field and the amplitude of stimulus movement. 2. Decrease of the amplitude of motion resulted in a decrease of direction selectivity index both in the group of direction-selective cells and in the group of cells classified as direction nonselective but with a directional bias. 3. The decrease of direction selectivity for small amplitude movement resulted mainly from increase in the magnitude of response in the nonpreferred direction of movement. 4. These results suggest that the receptive fields of most collicular cells are composed of subregions with different response profiles and indicate that inhibitory mechanisms dictate direction selectivity of collicular cells.

Corticofugal projections to the thalamus reveal 2 axonal morphologies, each associated with specific physiological attributes. These determine the functional characteristics of thalamic neurons. It is not clear, however, whether such features characterize the corticofugal projections that mediate multisensory integration in superior colliculus (SC) neurons. The cortico-collicular projections from cat anterior ectosylvian sulcus (AES) are derived from its visual, auditory, and somatosensory representations and are critical for multisensory integration. Following tracer injections into each subdivision, 2 types of cortico-collicular axons were observed. Most were categorized as type I and consisted of small-caliber axons traversing long distances without branching, bearing mainly small boutons. The less frequent type II had thicker axons, more complex branching patterns, larger boutons, and more complex terminal boutons. Following combinatorial injections of 2 different fluorescent tracers into defined AES subdivisions, fibers from each were seen converging onto individual SC neurons and indicate that such convergence, like that in the corticothalamic system, is mediated by 2 distinct morphological types of axon terminals. Nevertheless, and despite the conservation of axonal morphologies across different subcortical systems, it is not yet clear if the concomitant physiological attributes described in the thalamus are directly applicable to multisensory integration.

The main subcortical visual targets of retinal output neurones (ganglion cells) are the parvocellular and magnocellular layers of the dorsal lateral geniculate nucleus (LGN) in the thalamus. In addition, a small and heterogeneous collection of ganglion cell axons projects to the koniocellular layers of the LGN, to the superior colliculus (SC), and to other subcortical targets. The functional (receptive field) properties and target specificity of these non-parvocellular, non-magnocellular populations remain poorly understood. It is known that one population of koniocellular layer cells in the LGN (blue-On cells) receives dominant functional input from short-wavelength sensitive (S or ‘blue') cones. Here we asked whether SC neurones also receive S cone inputs. We made extracellular recordings from single neurones (n = 38) in the SC of anaesthetised marmoset monkeys. Responses to drifting and flashed gratings providing defined levels of cone contrast were measured. The SC receptive fields we recorded were often binocular, showed ‘complex cell' like responses (On–Off responses), strong bandpass spatial frequency tuning, direction selectivity, and many showed strong and rapid habituation to repeatedly presented stimuli. We found no evidence for dominant S cone input to any SC neurone recorded. These data suggest that S cone signals may reach cortical pathways for colour vision exclusively through the koniocellular division of the lateral geniculate nucleus.

We have examined whether the superficial layers of the superior colliculus (SC) provide the source of visual signals that guide the development of the auditory space map in the deeper layers. Anatomical tracing experiments with fluorescent microspheres revealed that a retinotopic map is present in the newborn ferret SC. Aspiration of the caudal region of the superficial layers of the right SC on postnatal day 0 did not cause a reorganization of this projection. Consequently, recordings made when the animals were mature showed that visual units in the remaining superficial layers in rostral SC had receptive fields that spanned a restricted region of anterior space. Auditory units recorded beneath the remaining superficial layers were tuned to corresponding anterior locations. Both the superficial layer visual map and the deeper layer auditory map were normal in the left, unoperated SC. The majority of auditory units recorded throughout the deeper layers ventral to the superficial layer lesion were also tuned to single sound directions. In this region of the SC, however, we observed much greater scatter in the distribution of preferred sound directions and a significant increase in the proportion of units with spatially ambiguous responses. The auditory representation was degraded, although many of these units were also visually responsive. Equivalent lesions of the superficial layers made in adult ferrets did not alter the topographic order in the auditory representation, suggesting that visual activity in these layers may be involved in aligning the different sensory maps in the developing SC.

1. The superior colliculus has been studied in intact cats and in cats with visual cortex lexions by recording the responses of single tectal units to visual stimuli. 2. Three classes of units have been identified in the superficial layers of the colliculus in these visually decorticate cats. 3. One class, comprising 5% of the units studied, has receptive fields organized concentrically in a manner similar to retinal ganglion cells. 4. The second class, comprising 12% of the units studied, responds to stimulus velocities over 300/sec, responds well to both small and large stimuli, and can be driven by strobe flashes at frequencies up to 35--40/sec. These units are termed 'flicker' cells. 5. The third class comprising 83% of the units studied, responds best to stimuli which are not larger than the activating region of the receptive field, moving at relatively low velocities. These units show strong suppressive surrounds which are sensitive to higher velocities of stimulus movement than the central activating region. Responses from the activating region in these units are dramatically inhibited by flickering dhanges in the level of background illumination. 6. In intact cats few units are found which are strongly inhibited by background flicker. 7. It is suggested that a high-velocity sensitive element such as the 'flicker' cell or phasic retinal ganglion cell is responsible for the flicker-induced inhibition of collicular units in the visually decorticate cat.