Figure 1.
Different spatial coding formats.
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).
Figure 2.
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.
Table 1.
Neural responses were tested for statistical significance during sensory and motor periods in comparison to the baseline period.
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).
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.
Figure 5.
Comparison of the goodness of fit for Gaussian versus sigmoidal functions.
Results for the population of SC neurons (A), with the color and symbol type indicating whether the Gaussian curve fit was significantly superior to that of the sigmoid (bootstrap analysis, p<0.05). Each neuron contributed 3 points to these panels, one for each fixation position. B. Simulation of the expected R2 values of Gaussian and sigmoidal curves if the underlying functions are Gaussian (left) vs sigmoidal (right). Units were simulated as Gaussians or sigmoids of varying parameters with noise, then fits were calculated for each unit and plotted in color indicating the location of the peak (left panel) or inflection point (right panel). The examples illustrate individual units with different peak or inflection point locations. (See: Table 2 for statistical significance of fits; Figure S1 for the experimental data limited to bimodal neurons; Figure S2 for the real data color coded by the eccentricity of the peak of the Gaussian or inflection point of the sigmoid; Figure S3 for data at sites tested with microstimulation; Figure S4 for the same data plotted as a correlation coefficient R for comparison with our previous studies [12], [15]; and Figure S5 for histograms of the difference between the Gaussian and sigmoidal R2; Figure S6 for the subset matching the visual and auditory responsiveness; and Figure S7 for the subset matching the visual and auditory response variability)
Table 2.
Summary of the statistical significance of the curve fits shown in Figure 5A.
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.
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.
Figure 8.
Read-out of SC motor activity for visual and auditory saccades.
A read-out model involving graded weights depending on the location of neurons in the SC (A). The weights were fit based on the motor activity on visual trials, combining all fixations and producing an eye-centered estimate of target location. B. Results of the simulation indicate that the model can successfully calculate target location from the input pattern, regardless of modality. C. Behavioral estimates of target location for visual and auditory trials (data from trials during the recording of the neurons). A slight compression of auditory space relative to visual space is seen both for the model (B) and the actual behavior (C).
Figure 9.
Schematic of the differences in activity evoked in the SC by visual and auditory targets.
When a target is visual, a "hill" of activity will be evoked at a location in the SC that corresponds to the visual response field of the neurons. Visual stimuli at different locations would evoke activity at different sites in the SC (A, B, C left panels). In contrast, auditory stimuli at different locations will evoke activity across the SC but with different discharge rates (A, B, C right panels). We note that this schematic does not address the code for the vertical dimension, nor does it consider the possibility that the inflection points of auditory response functions might vary with location in the SC. If the latter is true, then the auditory code would show some topography, with the edge of a broad hill varying with target location.