Maps are a mainstay of visual, somatosensory, and motor coding in many species. However, auditory maps of space have not been reported in the primate brain. Instead, recent studies have suggested that sound location may be encoded via broadly responsive neurons whose firing rates vary roughly proportionately with sound azimuth. Within frontal space, maps and such rate codes involve different response patterns at the level of individual neurons. Maps consist of neurons exhibiting circumscribed receptive fields, whereas rate codes involve open-ended response patterns that peak in the periphery. This coding format discrepancy therefore poses a potential problem for brain regions responsible for representing both visual and auditory information. Here, we investigated the coding of auditory space in the primate superior colliculus(SC), a structure known to contain visual and oculomotor maps for guiding saccades. We report that, for visual stimuli, neurons showed circumscribed receptive fields consistent with a map, but for auditory stimuli, they had open-ended response patterns consistent with a rate or level-of-activity code for location. The discrepant response patterns were not segregated into different neural populations but occurred in the same neurons. We show that a read-out algorithm in which the site and level of SC activity both contribute to the computation of stimulus location is successful at evaluating the discrepant visual and auditory codes, and can account for subtle but systematic differences in the accuracy of auditory compared to visual saccades. This suggests that a given population of neurons can use different codes to support appropriate multimodal behavior.

Funding: This work was supported by the National Institutes of Health (NS50942)( http://grants.nih.gov/grants/oer.htm ). JL was also supported by the Korea Research Foundation Grant funded by the Korean Government (KRF-2008-356-H00003)( http://www.krf.or.kr/KHPapp/eng/maina.jsp ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

In this study, we investigated the coding format of auditory responses of rostral SC neurons in detail, to ascertain whether they exhibit a clos­ed-field organization, similar to visually-driven activity in this structure and consistent with the formation of a map of space, or whether they are open-field, like the response patterns of neurons in the auditory areas that serve as inputs to the SC, and therefore potentially consistent with a rate code for sound location.

However, other ways of encoding stimulus position are known to exist, particularly for auditory information in primates. In monkeys and humans, auditory-responsive neurons in areas upstream from the SC do not appear to have such bounded receptive fields distributed across the scene [12] – [17] . Instead, their “receptive fields” exhibit an open-ended structure in frontal space with peak activity in the periphery. These open-ended response patterns likely derive from interaural timing and/or level differences, which reach their maximum values for sounds located along the interaural axis ( Figure 1B , peaks of “receptive fields” cluster at left and right poles). Sound location would then be encoded not by the identity of the active population but by the level of neural activity, being proportional to the sine of the azimuthal location of the stimulus. Such a code is referred to as a rate code for sound location.

When sampling of space must be limited to the oculomotor or visual ranges, maps and rate codes for sound azimuth can be distinguished by evaluating whether neurons exhibit circumscribed receptive fields (A) or open-ended response functions (B). Rate coding neurons might show some degree of non-monotonicity if their underlying tuning functions were not all perfectly aligned with the interaural axis (dotted line).

The aligned-map hypothesis of multisensory integration presupposes that auditory space is indeed encoded via a map. Such a map should contain neurons whose receptive fields tile the entire expanse of space ( Figure 1A ). When tested with stimuli limited to the oculomotor or visual ranges - the ranges of space relevant for the rostral, eye movement-related, superior colliculus - the signature feature that best characterizes maps is that such receptive fields would appear circumscribed. That is, a sizeable population of neurons should have peak responses at some best target location within the tested range, and much lower responses for targets on either side. Sound location would be reflected in the identity of the active population.

Results

Behavioral paradigm and analysis of neural data We assessed the responses of SC neurons (n = 180) in monkeys making eye movements to visual and auditory targets. Target locations spanned a range of +/− 24° with respect to the head from three initial fixation positions (−12, 0, 12°, for a range of +/− 36° with respect to the eyes.) Monkeys (n = 2) performed an overlap saccade task (Figure 2; see also Materials and Methods) allowing sensory-related activity (0−500 ms after the target) to be dissociated from saccade-related activity (20 ms before saccade onset to 20 ms before saccade offset). We selected neurons that responded significantly for at least one target modality (n = 175, 97% out of 180 neurons, Table 1) for further study. The majority were bimodal (63 and 84% for sensory and saccade-related activity respectively). The response patterns for visual stimuli were predominantly eye-centered during both the sensory and motor periods, whereas auditory activity shifted from hybrid to eye-centered coordinates as the saccade approached. This and other aspects of this neural data set are described in more detail in our previous study [18]. To eliminate reference frame as a factor for the present study, we conducted all analyses separately for each fixation position. PPT PowerPoint slide

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larger image TIFF original image Download: Figure 2. Experimental design. Spatial layout of the targets (A) shows that the fixation targets (black dots) were located 12° left, 0° and 12° right at varying elevations depending on the spatial sensitivity of the neuron under study ranged −16 to 6 degree (mean±SD: −4.2±4,1). Targets were either auditory (white noise burst) or visual (LED), presented from a stimulus array of 9 speakers each with an LED attached to its face. The speakers were spaced from 24° left to 24° right with 6° intervals at an elevation of 0° with respect to the animal’s head. B. Events of the overlap saccade task. The baseline period was 500 ms before target onset, the sensory period was 0−500 ms after target onset, and the motor period began 20 ms before saccade onset and ended 20 ms before saccade offset. C. The no-saccade task was similar except that the targets were near or beyond the oculomotor range, and the animal was not required to make an eye movement because the fixation light stayed on. https://doi.org/10.1371/journal.pone.0085017.g002 PPT PowerPoint slide

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larger image TIFF original image Download: Table 1. Neural responses were tested for statistical significance during sensory and motor periods in comparison to the baseline period. https://doi.org/10.1371/journal.pone.0085017.t001

Differences between visual and auditory spatial coding Neurons showed different spatial response properties depending on whether the target was visual or auditory. For visual stimuli, the classic circumscribed receptive field pattern was evident: responses were largest for a particular target eccentricity, but fell off substantially for targets located both more centrally and more peripherally. For example, the neuron shown in Figure 3A shows a peak response for visual targets/saccades at about 18 degrees contralateral, for both the sensory and motor period. Activity for both larger and smaller amplitude target displacements (e.g. 0 or 40°) is considerably lower. The visual responses of the neuron in Figure 3B are similar. PPT PowerPoint slide

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larger image TIFF original image Download: Figure 3. Two representative SC neurons (A, B) showing different sensitivity for visual and auditory stimuli (mean discharge rate +/− standard error with respect to the horizontal eye-centered target location or movement amplitude; S R2 and G R2 refer to the Sigmoidal and Gaussian R2 values). For three out of the four visual responses (upper panels), the fits of Gaussian function are significantly better than those of sigmoidal function (the sensory R2 values for A and B, and the motor R2 value for B; bootstrap analysis, p<0.05). In contrast, for the auditory responses (lower panels), the fit of both functions are about equally good (bootstrap analysis, p > 0.05). https://doi.org/10.1371/journal.pone.0085017.g003 In contrast, for auditory stimuli, responses typically showed an open-ended pattern. As target eccentricity increased, responses either continued to increase, reached a plateau, or showed only a modest dip in activity. The auditory motor responses of the neuron in Figure 3A (bottom right panel) reached a plateau at around 15° and did not drop from this level. The auditory responses of the neuron in 3B show a small decrease in activity for larger amplitude targets (Note that the visual and auditory responses within 3A are from the same neuron, as are those of 3B for a different neuron). A difference is also evident in the “point image” of activity evoked on visual vs auditory trials. Figure 4 shows the average activity on auditory trials normalized to that observed for visual trials as a function of target location within the contralateral hemifield. The relative activity levels increase with increasing target eccentricity, both during the sensory period and during the motor period, For the most eccentric targets (>30°), auditory motor-related activity could exceed that observed for visual targets (y values greater than 100). PPT PowerPoint slide

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larger image TIFF original image Download: Figure 4. “Point image” of auditory activity in comparison to visual activity as a function of target location. For each neuron, we calculated the activity for a given target location, modality, or response period as a proportion of the peak firing rate observed for any target location, modality, or response period for that neuron. We then calculated the average of this normalized activity across the population of neurons as a function of target modality and target location. This graph plots the average normalized population activity on auditory trials as a percentage of that observed on visual trials. (Only locations in the contralateral hemisphere are shown because visual activity is very low or non-existent for ipsilateral targets, which would make even modest auditory activity appear very large in comparison.) A value of 100 (horizontal dotted line) indicates that the activity for visual and auditory stimuli at the corresponding target location was about equal. As target location becomes more eccentric, the level of activity evoked by auditory stimuli during the motor period approaches and then slightly exceeds that observed for visual stimuli (solid line). A similar increase in auditory activity relative to visual activity with target eccentricity is observed during the sensory period (dashed line), but at an overall lower level. https://doi.org/10.1371/journal.pone.0085017.g004

Comparison to simulated data To verify that this comparison between Gaussian and sigmoidal fitting can successfully distinguish between such response patterns, we tested the curve fitting procedure on simulated Gaussian and sigmoidal data plus noise (see Materials and Methods). For open-ended response patterns simulated with sigmoids, the sigmoidal and Gaussian curve fits were equally successful (the R2 values are essentially identical and the data lie along the line of slope one, Figure 5B right panel) and the resulting curves are indistinguishable from each other in shape (examples). For circumscribed receptive fields simulated with Gaussians, the Gaussian fits tended to yield higher R2 values than did the sigmoidal fits (data points largely above the line of slope one, Figure 5B left panel). The relative advantage of a Gaussian fit depended on the eccentricity of the peak location with respect to the sampled range. For simulated units tuned to the center location, the Gaussian provided a much better fit (see examples). In contrast, if a simulated neuron’s peak tuning was more peripheral with respect to the sampled range, the sigmoidal function also yielded a good fit.

Controlling for the sampling range Could the apparent open-ended auditory response patterns in the actual neurons therefore be an artifact of failing to sample circumscribed auditory receptive fields at sufficiently eccentric locations? Several points argue against this interpretation. First, the visual and auditory targets occupied the same locations, so the sampling of visual and auditory space was identical. If the sampling was insufficient to observe circumscribed auditory receptive fields, it should also have been insufficient for visual receptive fields. Second, the sampling was matched to the range of space where circumscribed receptive fields should have been found if they existed. The targets spanned the portion of the oculomotor range of monkeys that is not normally accompanied by head movements [19]. Furthermore, we concentrated on sampling the rostral region of the SC which codes for smaller saccades: the mean stimulation-evoked horizontal component of saccade amplitude at 19 representative sites was 6.4°, and the range was from 0.67 to 15.2° horizontally (see Materials and Methods)(Figure S3). Thus our sampling, out to 36° in eye-centered coordinates generally extended beyond the expected range of peak movement field locations in our neural data. Nevertheless, we took two additional steps to address this question. First, for the sensory period, we expanded the sampling extent by including some non-saccade trials involving targets near or beyond the limits of the oculomotor range but still within the visual scene (148 neurons, targets: ±30° ±42° and ±60° in addition to original target locations. This corresponds to a range of ±72° in eye-centered coordinates). These trials were included on 15.6±10.4% (mean±SD) of the trials and differed only in that the fixation light stayed on and no saccade was required, which allowed us to investigate the sensory period but not the motor period. Figure 6A illustrates an example neuron, showing peaked tuning for visual stimuli but monotonic sensitivity to sound, and Figure 6B shows that the overall population pattern is very similar to that seen for the more limited target sampling within the oculomotor range in the main data set. PPT PowerPoint slide

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larger image TIFF original image Download: Figure 6. Two tests of the effect of sampling range. Results in for an example neuron (A) tested out to 72° relative to the eye (ipsilateral fixation, interleaved non-saccade task). B. Population results, format similar to the corresponding panels of Figure 5A. C. Results for the motor period when excluding visual saccades that did not match the auditory saccade range. Only bimodal motor neurons are included in these panels; no bootstrap analysis of these curve fits was performed due to the limited numbers of trials available. All other details are as in Figure 5A. https://doi.org/10.1371/journal.pone.0085017.g006 Second, for the motor period, we corrected for any effects that systematic differences in visual vs. auditory saccade accuracy might have introduced to the sampling range. Auditory saccades can show some systematic biases like undershooting or upward shifts [20], [21] A tendency to undershoot auditory targets might cause us to undersample the more eccentric movement amplitudes for sounds, and would bias us towards concluding open-ended coding for sounds. Accordingly, to ensure matched visual and auditory sampling, we limited the visual data to visual saccades that spanned the same range of space as the auditory saccades. Visual saccades to a given target that lay more than one standard deviation away from the mean auditory saccade endpoint for that target were excluded from the analysis. Some cells had to be eliminated from the analysis due to too few trials (i.e. if fewer than 20% of the trials were left from the original), and the reduction in number of trials prevented use of the bootstrap analysis. But among those neurons that remained (N = 56 cells), the overall pattern was the same (Figure 6C). If the apparent open-ended response patterns on auditory trials were due to inadequate sampling, then when we make the visual sampling identical to the auditory sampling, the visual code should look open-ended too, but it does not.

Comparison of monotonicity for visual vs. auditory stimuli Although the auditory response patterns were generally open-ended, they were not always perfectly monotonic. In some neurons, the responses for the most contralateral target were a little lower than they were for sounds at more intermediate locations (Figure 7A, also see Figure 3A). However, this drop-off was usually small and could have been due simply to variability in neural responses. PPT PowerPoint slide

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larger image TIFF original image Download: Figure 7. Monotonicity index methods and results. An example neuron showing a drop-off in responses at the most contralateral target positions (sensory responses shown) (A). We compared the responses at the peak location to the responses at the most contralateral location (black dots) and expressed the result as a Z-score (inset). Data for the ipsilateral fixation was used for this analysis. B. The distribution of Z scores for each modality (grey bars), in comparison to the Z scores expected if the relationship between activity and target location is scrambled (Monte-Carlo simulation, black bars). The dotted lines illustrate the 95% confidence threshold; real Z scores to the right of this point are considered to show statistically significant decrements in activity for more peripheral targets (p<0.05) C. The proportion of neurons showing significant non-monotonicity. D. Same as C, but for targets limited to different cut-off points in our sampling range. The disparity between visual and auditory non-monotonicity is present for all cut-offs, and only with a 36 degree cutoff for sound does the level of non-monotonicity reach that seen for a 12 degree cutoff for visual stimuli. https://doi.org/10.1371/journal.pone.0085017.g007 To determine how often the drop-off exceeded chance variation, we compared the activity at the most contralateral position with the activity at the best location (Figure 7A and inset). For the neuron shown in Figure 7A, the best location on visual trials (top) was about 6 degrees to the right, and the activity evoked at that location exceeded that for the most contralateral location by about 30 spikes per second. On auditory trials (bottom), the best location was about 24 degrees to the right, exceeding the most contralateral location's response by about 10 spikes per second. To take into consideration the variability in as well as the difference in responsiveness, these values were then converted to a Z score (Figure 7A inset). A large Z score indicates a large drop from the peak activity for more eccentric targets, i.e. a non-monotonic (circumscribed) response pattern. The visual response pattern of the neuron shown in Figure 7A had a Z score of 1.84, whereas the auditory response pattern's Z score was 0.57. These values mean little on their own, but can be compared to the expected distribution of the Z scores under chance. To calculate this distribution, we performed a Monte-Carlo simulation in which the actual target location was scrambled (Figure 7B). For auditory responses, only about 21% of neurons showed a non-monotonic pattern or Z score that was significantly larger than expected by chance (i.e. was greater than 95% of the scrambled Z scores, dotted vertical lines, p<0.05, grey bars in top panels of Figure 7B). In contrast, almost 60% of visual responses met the criteria (grey bars in bottom panels of Figure 7B). The percentages of neurons exceeding criteria are illustrated in Figure 7C. This discrepancy between the visual and auditory patterns remains even when data for more eccentric locations are excluded, limiting the sampling to smaller ranges (Figure. 7D), again suggesting that sampling range cannot account for the pattern of results.