Weights were also computed by combining responses at two or more reference sound levels—a calculation which mainly affected estimates for 2nd-order weights. A model with combined sound levels usually predicted rate responses to RSS stimuli (but not HRTF stimuli) more accurately than models estimated at only one sound level: median improvements in the goodness of fit for the predictions (i. e., *fv*; defined in the next section) are 0.12, 0.15, and 0.12 for I, V, and O neurons, respectively. The performance improvement presumably reflects the fact that a larger amount of data (400 vs. 200 data points) and a wider range of stimulus levels were used to fit the model. Response predictions generated by combining data from multiple levels are mixed with single-attenuation fits in **Figures 2C**, **3C**, **4C**, **7**, **9**.

The frequencies that are assumed to contribute to a neuron's responses are additional parameters in the model. These parameters, which are shown as limits on the summations {*n*1, *n*2, *n*3, *n*4, *n*5, *n*6} in Equation 1, define the sequential set of weights (including BF) that lead to the model's best prediction performance. These frequency limits are determined by systematically varying the span of frequencies used during weight estimation and prediction. Parameters {*n*1, *n*2} are varied first, followed by {*n*3, *n*4} and finally {*n*5, *n*6}. Specifically, beginning with one weight at BF, weights are added below and above BF, one at a time, only as long as they improve prediction performance. When the 2nd-order weight limits are being determined, the 1st-order weight limits are fixed at the best performance of the 1st-order model. Similarly, when the binaural weight limits are being determined, both the 1st- and 2nd-order weight limits are fixed. Note that all weights in the current set, as specified by the {*nj*}, are estimated simultaneously at each step, so that as 2nd-order weights are added, the 1st-order weights usually change. Neither the weights nor the prediction error are strong functions of {*nj*}, especially near the edge of the response area, so the order in which the {*nj*} are tested does not change the model or error.

#### **PREDICTION**

Increasing the limits on summation in the model amounts to increasing the number of weights that need to be estimated. To prevent overfitting the model, we consider only weights in the model that are needed to accurately predict responses to broadband stimuli. To test the model, we use a leave-one-out cross-validation procedure where each of the 200 rates is set aside as a test response while the remaining 199 rates are used to estimate weights. The procedure is repeated for each of the 200 rates. This method produces results similar to bootstrap; however, it is preferred over bootstrap because it yields 200 separate prediction tests while also using the largest number of data points to fit the model. The SEMs of the weights are estimated from the leave-oneout calculations as *(n* − 1*)* σ*p/* √*n*, where *n* is the number of data points (200) and σ*p* is the standard deviation of the weights in the 200 leave-1-out trials [Equation 10.9 in Efron and Tibshirani (1986)].

The quality of prediction is measured with fraction of variance, defined as:

$$f\nu = 1 - \frac{\sum \left(r\_{\circ} - r\_{m\circ}\right)^2}{\sum \left(r\_{\circ} - \hat{r}\right)^2} \tag{2}$$

where *rj* are the rates in the test data set, *rmj* are the rates predicted by the model, and *r*ˆ is the mean of the *rj*. The value of *fv* is 1 for a perfect fit and decreases as the fit worsens. The *fv* is zero if the model predicts the data no better than the mean rate, and the *fv* can be negative for very bad fits. We have chosen *fv* as a measure of fit because, unlike the often-used correlation coefficient (*R*), *fv* is sensitive to constant rate errors (i. e., differences in the mean rates between model and data). Such errors can be a problem for a model with even-order terms. Empirical comparisons of *fv* and *R*2 based on the present data and auditory nerve data (Young and Calhoun, 2005) show that *R*2 ≥ *fv* in all cases, and that the two measures are approximately equal for good fits (taken to be values of *fv* above ∼0.5). Noise in the rate measurements cannot be fit by the model and therefore decreases *fv*. A Poisson assumption for the rate statistics suggests that the maximum possible *fv* is about 0.8—a value consistent with the data (**Figure 7B**, assuming values above 0.8 are random scatter).

During this fitting process, *fv* for the 1st-order model usually did not change dramatically as the number of weights (*n*1 and *n*2) changed. However, the addition of 2nd-order weights to the bestperforming 1st-order model (i. e., the 1st-order model with the largest *fv*) usually yielded increases in *fv* by up to 0.4, even when only one 2nd-order weight was introduced. The inclusion of additional 2nd-order weights (beyond the first one) also produced only small incremental changes in *fv.* This behavior, which was observed in 314/337 cases, suggests that the 2nd-order model provides information about spectral integration in these neurons that is not present in the 1st-order model. Note that binaural weights usually added little to the prediction quality of the best 1st- or 2nd-order models—improving *fv* by less than 0.01 in 225/310 cases.

Weight function models derived from RSS responses were used to predict responses to HRTF stimuli. Because the HRTF stimuli did not have an all-0-dB reference for the estimation of *R*0, a modified prediction procedure was used. For these predictions, the reference stimulus was chosen to be the average of the four HRTF stimuli with the flattest spectral shape within 0.5 octave of the neuron's BF. *R*0 was set to the average of the rates produced by those stimuli. HRTF responses were then predicted as differences between the response to the averaged reference stimulus and responses to each of the other 96 HRTF stimuli. This method produced usable predictions, but there were still errors in the rate predictions; specifically, there was often a shift in the means of the actual and predicted rates (see **Figure 8B**). As such, *R*2 (i.e., the square of the Pearson product-moment correlation coefficient), and not *fv*, was used to quantify errors between the actual and predicted rates (in **Figure 8C** only). Unlike *fv*, *R*2 is sensitive to correlated rate fluctuations but not to errors in average rate or overall rate gain.

#### **SECOND-ORDER FILTERS**

The frequency selectivity implicit in the 2nd-order weights may be better understood by interpreting the terms in Equation 1 as filters (e.g., Lewis et al., 2002; Reiss et al., 2007). Equation 1 can be written in vector-matrix form as:

$$r = R\_0 + \vec{\boldsymbol{w}}\_C^T \vec{\boldsymbol{s}}\_C + \vec{\boldsymbol{w}}\_I^T \vec{\boldsymbol{s}}\_I + \vec{\boldsymbol{s}}\_C^T \mathbf{M}\_C \vec{\boldsymbol{s}}\_C + \vec{\boldsymbol{s}}\_I^T \mathbf{M}\_I \vec{\boldsymbol{s}}\_I + \vec{\boldsymbol{s}}\_C^T \mathbf{M}\_B \vec{\boldsymbol{s}}\_I \tag{3}$$

where the stimulus vectors *sC* and *sI* are defined above. Weight vectors *wC* and *wI* contain the 1st-order weights. Matrices **M***C*, **M***I*, and **M***B* contain 2nd-order and binaural weights corresponding to frequency pairs. Because the 2nd-order matrices **M***C* and **M***I* are real and symmetric, they can be written as

$$M = \sum\_{j=1}^{N} \lambda\_j \vec{e}\_j \vec{e}\_j^T \tag{4}$$

where λ*j* are real eigenvalues of **M** and *ej* are orthonormal eigenvectors. As such, each 2nd-order term in Equations 1 and 3 can be written as:

$$\vec{s}^T \mathbf{M} \vec{s} = \vec{s}^T \left(\sum\_{j=1}^N \lambda\_j \vec{e}\_j \vec{e}\_j^T\right) \vec{s} = \sum\_{j=1}^N \lambda\_j \left(\vec{s}^T \vec{e}\_j\right)^2. \tag{5}$$

Examination of Equation 5 shows that an eigenvector *ej* can be viewed as an "equivalent 2nd-order filter." However, because the filtering operation *s Tej* is squared, the excitatory or inhibitory nature of the 2nd-order filter is determined by the sign of the eigenvalue, where a positive eigenvalue indicates an excitatory contribution and a negative eigenvalue indicates an inhibitory contribution. Furthermore, the magnitude of the eigenvalue quantifies the importance of the associated filter. In this paper, filters are shown for only the largest one or two eigenvalues.

### **RESULTS**

Neurons in the CNIC show a variety of patterns of frequency sensitivity, as measured by responses to tones [reviewed by Davis (2005)]. Here, we use response types described by Ramachandran and colleagues in decerebrate cat (Davis et al., 1999; Ramachandran et al., 1999), as detailed in Materials and Methods. As stated previously, there are three types of tonic responses: types V, I, and O. **Figures 2** through **4** provide examples of tone and broadband tuning for these neuron types. Data presented in this paper are from neurons for which complete tone and RSS responses were obtained across a range of sound levels. These include 12 V neurons (BFs 0.8–4.1 kHz), 13 I neurons (BFs 1.8–21 kHz), and 10 O neurons (BFs 2.2–21 kHz). Onset neurons were also encountered during the study—comprising 9% of the total sample—and all gave reliable tonic responses to RSS stimuli. However, onset neurons were not studied completely and are not discussed here.

## **RESPONSE MAPS**

#### *Type I tuning*

**Figure 2A** shows a tone response map typical of a type I neuron. Contralateral response maps (left) show a narrow excitatory area centered on BF (in this case, 12.2 kHz) and inhibitory sidebands at frequencies below and above the excitatory area (light blue). To better visualize the inhibitory areas, the response at −40 dB is shown on an expanded scale at the bottom of **Figure 2A**. Ipsilateral response maps (right), by contrast, show mainly inhibitory areas that are well tuned and centered on approximately the same BF. Like all type I neurons that were analyzed in the study, this neuron is EI in its responses near BF.

The effective tuning of a type I neuron for broadband noise can be inferred from the 1st-order weight functions (**Figure 2B**). First-order weight functions computed for the contralateral ear (at left) resemble contralateral response maps in that they show large positive peaks at BF. Similarly, 1st-order weight functions for the ipsilateral ear are negative at frequencies where responses to ipsilateral tones are inhibitory. This pattern of excitation and inhibition inferred by the weight-function maps is typical of all type I neurons studied.

To better understand the nature of 2nd-order weight functions, eight 2nd-order filters (Equation 5) were computed for the type I neuron featured in **Figure 2**. At low sound levels, negative eigenvalues for this neuron are small compared to the positive eigenvalues—a finding that supports a predominantly excitatory contribution of 2nd-order weights. However, inhibitory 2nd order effects are present at higher sound levels, where negative and positive eigenvalues reach comparable values (*<*0.1, not shown). The two filters corresponding to the largest positive eigenvalues are shown in **Figure 2D**. As indicated by the shaded areas (±1 SEM), most of the second-order weights are significantly different from zero. First-order weight functions at the same sound levels are superimposed for comparison (black dashed lines). Some of the contralateral 2nd-order filters are shown to have plus-minus shapes that differ significantly from 1st-order weight functions. For example, peaks are present at frequencies below BF rather than on BF, and substantial gain slopes occur near the BF. These differences suggest that 2nd-order filters change the frequency selectivity of the neuron.

The meaningfulness of the second-order weight function was evaluated by testing how well the model predicted responses to RSS stimuli not used in parameter estimation. Predictions were performed at the same sound levels featured in **Figure 2B**, and the results for two sound levels are shown (**Figure 2C**). Here, each data point is the predicted rate response to each left-out test stimulus (ordinate), plotted against the actual rate response to that stimulus. The *fv*-values (Equation 2), which measure the accuracy of the prediction, are 0.85 and 0.79 at the sound levels shown. For this type I neuron, the contribution of the 2nd-order terms in the weight-function model is significant: when all 2nd-order weights are excluded from the model, *fv*-values decrease to 0.32 and 0.58, respectively.

#### *Type O tuning*

Response maps and weight functions of a type O neuron are shown in **Figures 3A,B**, respectively. At low sound levels, the contralateral response map for the type O neuron (**Figure 3A**, left) shows an excitatory response to low-level tones near BF (11.4 kHz at −90 dB); at higher levels, the response map demonstrates a strong inhibitory response around BF, and a mixture of excitatory and inhibitory responses at frequencies away from BF. Given that the responses to ipsilateral tones (**Figure 3A**, right) were predominantly inhibitory, the neuron was classified as EI, as were all type O neurons studied. Unlike the type I neuron (**Figure 2**), contralateral weight functions computed from responses to RSS stimuli (**Figure 3B**, left) differ significantly from the tone-based response map: rather than having large areas of inhibition at BF, the weight functions show a combination of positive gains near BF and negative gains above BF across a range of sound levels. Ipsilateral weight functions (**Figure 3B**, right), on the other hand, are similar to the response maps in that they mainly show weak inhibition near BF at most sound levels.

The equivalent 2nd-order filters show substantial inhibitory and excitatory contributions (**Figure 3D**), possessing both large negative (blue filters), and large positive eigenvalues (red filters). The contralateral and ipsilateral 2nd-order filters are statistically different from zero, and their shapes differ significantly from their corresponding 1st-order weight functions.

Compared to the weight-function model for the type I neuron, the model for the type O neuron is a less accurate predictor of responses to novel stimuli (**Figure 3C**). For this type O example, the general trend in the data is captured by the model; however, the *fv*-values are only 0.54 and 0.51 at the two sound levels shown. The 2nd-order terms are important to the model, as exclusion of these terms produces *fv*-values (for the best-fitting 1st-order model) of only 0.38 and 0.28, respectively.

#### *Type V tuning*

Like all type V neurons in this study, the neuron in **Figure 4** is EE, exhibiting broad excitatory responses to contralateral and ipsilateral tones (**Figure 4A**). At high sound levels, the weak response near BF, in addition to apparent limits on the spread of excitation to frequencies above BF, suggests that the response of the neuron is sculpted by inhibition. Although this behavior is observed in many V neurons (Davis et al., 1999), responses to tones that are strictly inhibitory (i. e., with rates below spontaneous rate) are small and rarely observed. Note that type V neurons were also encountered for which either, or both, response maps did not show signs of inhibition (not shown).

At low sound levels, weight functions for the type V neuron in **Figure 4B** resemble tone response maps in that weights are significantly positive at BF. Weight functions at higher sound levels show negative weights around BF in a manner consistent with the inhibitory sculpting described above. Second-order filters (**Figure 4D**) corroborate the inhibitory nature of this neuron's response: the largest eigenvalues are negative, and increases in sound level produce even larger negative eigenvalues.

In **Figure 4C**, the weight-function model predicts responses of the type V neuron with moderate accuracy. The *fv*-values for the 2nd-order model are 0.61 and 0.41 at the two sound levels shown. Exclusion of 2nd-order weights from the model reduces *fv*-values to 0.54 and 0.26, respectively.

#### **LEVEL TOLERANCE**

In this section, we compare the tuning of neurons responding to tones against the tuning implicit in responses to RSS stimuli. Specifically we compare areas of activity observed in tone response maps with those seen in weight-function maps at equivalent sound levels (dB re threshold). For type I neurons, this comparison is robust and straightforward. In the tone maps of type I neurons, edges of activity are taken to be the frequencies at which rate responses first go to zero on either side of BF (**Figure 5A**, blue line). These edges define a central excitatory region flanked by inhibitory sidebands. Analogously, in weightfunction maps, edges of activity are taken to be the frequencies at which 1st-order weights are first indistinguishable from zero on either side of BF (i.e., within ±1-SEM of zero; **Figure 5B**, dashed green lines). In this example, as sound level increases, tone-based tuning (blue curve) broadens, as is commonly observed for auditory neurons in many parts of the brain. By contrast, the tuning apparent in noise-based weight-function maps is level tolerant: that is, bandwidths are relatively fixed over the range of sound levels studied (dashed green curve).

The comparison of tone- and noise-based tuning in **Figure 5** is typical of type I neurons. This is shown in **Figure 6A** where upper and lower edges of tuning curves are shown for type I

**FIGURE 5 | Derivation of tuning curves from response maps for a type I neuron (***BF* **= 8.3 kHz). (A)** Tone response map for the contralateral ear. The blue curve shows the tuning curve edges for the tone response map, derived as described in the text. The green dashed curve in both **(A)** and **(B)** shows the edges of the tuning curve for the weight-function map. **(B)** Weight-function map for the same neuron. The dB scale in both parts is dB re threshold for BF tones **(A)** or RSS stimuli **(B)**. The tuning curve near the BF tip is not well specified by the response maps. For the tone response maps, the threshold at BF is determined from a rate-level function; for the weight-function maps, the threshold is set halfway between levels that do and do not produce weights that are significantly different than zero.

**response maps for populations of type I (A), O (B), and V (C) neurons.** Frequency edges computed as in **Figure 5** are shown for *contralateral* stimuli only. Lower-(left) and upper-(right) frequency edges derived from tone response maps (blue lines) are overlaid on those derived from weight-function maps (dashed green lines) at equivalent sound levels, in dB re threshold. The point at BF is not included. For each neuron, lower (or upper) edge frequencies are plotted relative to the geometric mean across levels of the lower (or upper) edge frequencies of the weight-function maps. Slopes of lower frequency edges of tone maps differ significantly from those observed in weight-function maps (type I: *P* = 0*.*1; type V: *P <* 0*.*01; signed

neurons. Frequency edges derived from weight-function maps (green dashed curves, as in **Figure 5**) are overlaid on those derived from tone-based response maps (blue lines, as in **Figure 5A**). To enable comparison of tuning curve slopes across neurons with different BFs, lower (or upper) frequency edges for each tuning curve (i. e., tone response maps and weight-function maps) are plotted relative to the geometric mean of lower (or upper) edge frequencies across all sound levels of the weight-function map. The lower and upper edges are plotted separately and the point at BF is not included.

Type I tuning in weighting functions is level tolerant: as sound level increases, upper and lower frequency edges remain relatively fixed. Level tolerance, however, was not observed in tone response maps, where increases in sound level typically yielded a broadening of response maps at the lower frequency edge: the slopes differ significantly between tone and weight-function maps on the low-frequency side (*P* = 0*.*1, Bonferroni corrected signedrank-sum comparisons of slopes of best-fit lines), but not the high-frequency side.

For type O neurons, a direct comparison of tone- and noisebased tuning was not done because the patterns of excitation and inhibition in tone maps are complex and vary significantly from sound level to sound level (as in **Figure 3A**); thus, it is not clear how a meaningful and consistent bandwidth measure would be chosen. However, weight-function maps could be analyzed as not differ. Tone maps were not analyzed for type O neurons (see text). The red curves in **(B)** show mean weight-function edges for auditory nerve fibers [ANF; data from Figure 5 of Young and Calhoun (2005)]. Here, frequencies are normalized by the average frequency edge at the lowest two sound levels. As sound level increases, the slopes of lower-frequency edges of CNIC weight-functions differ from those observed in ANF. Specifically, slopes suggest a relative narrowing in type I and type O data (*P* = 0.02), and relative widening in type V neurons (*P* = 0*.*07). For all weight-function types, upper frequency edges are not significantly different from those seen in ANFs. All Ps are Bonferroni corrected.

described above for type I neurons. Because type O neurons often have distinct excitatory *and* inhibitory regions that are prominent near BF (as in **Figure 3B**, 3/7 cases), frequency edges in type O maps were chosen to include all statistically significant features that persisted across sound level (i. e., weights exceeding ±1 SEM). For the map in **Figure 3B**, the persistent feature that was selected consists of both the excitatory lobe near BF and the inhibitory lobe above BF. The choice of persistent feature is not critical, for as long as the same feature definition is applied consistently across sound level, the result does not change for the sample of type O neurons studied. In **Figure 6B**, upper and lower frequency edges are shown for a population of type O neurons. Note that the frequency edges are roughly constant at the different sound levels, thus implying that broadband frequency selectivity in type O neurons is tolerant to increases in noise level.

A similar analysis for a population of type V neurons is shown in **Figure 6C**. As with type I neurons, edges of activity in tonebased response maps were taken to be frequencies at which rate responses first go to zero on either side of BF. This area of activity included frequencies at and above BF where responses were excitatory but appear dampened by inhibition (e. g., **Figure 4A**). As with type O neurons, frequency edges for weight-function maps were defined to encompass distinct statistically significant features. However, unlike type O neurons, the excitatory and inhibitory nature of those features often changed with changes in sound level, as in **Figure 4B**. In such cases, all features near BF were included as part of a response area if they persisted over at least two levels. Because of the variability in weight-function maps, there is considerable variation in the width of type V tuning with changes in sound level (green dashed curves in **Figure 6C**). Weight-function tuning usually broadens near threshold, and in some cases narrows at high sound levels. However, lower frequency edges of weight-function maps remained relatively fixed compared to lower frequency edges of tone-based response maps (*P <* 0*.*01, signed rank sum test comparisons of slopes of best-fit

lines). Thus, the three CNIC neuron types—I, O, and V—exhibit level-tolerant tuning in response to broadband stimuli, but not in response to tones. For comparison, the edges of weight-function maps for a population of ANF were averaged and plotted in **Figure 6B** (red). For ANFs, the upper and lower frequency edges delineate an area of excitation that is centered on BF. The ANF weight-function map broadens slightly with increasing noise level at the lower frequency edge, exhibiting more widening than observed in type I and O edges (*P* = 0*.*02; signed rank sum test of slopes of best-fit lines). By contrast, ANF tuning widens less than that of type V neurons (*P* = 0*.*07).

#### **QUALITY OF MODEL PREDICTIONS AND THE IMPORTANCE OF 2ND-ORDER TERMS**

The weight-function model in Equation 1 predicts responses of the three CNIC neuron types with different degrees of accuracy. **Figure 7A** shows the distributions (left) and cumulative distributions (right) of *fv*-values for predictions computed using the leave-one-out procedure. Of the three response types, the model performs best for type I neurons where the median value of *fv* is 0.51 (gray). For the type V and O populations, median values of *fv* are only 0.30 (red) and 0.21 (blue), respectively. The differences in the medians of the *fv* distributions are statistically significant (see the figure caption).

The accuracy of the model predictions depends substantially on the inclusion of 2nd-order terms. The scatterplot of **Figure 7B** compares the performance of two models for each test response a model which includes all 1st- and 2nd-order terms (abscissa), and another which includes only the 1st-order terms (ordinate). All data points are located below the diagonal line, meaning that the addition of 2nd-order terms consistently improves model performance. This was found to be true regardless of the quality of the 1st-order fit or the neuron type. Specifically, the median improvement in *fv* (calculated as the difference in *fv*-values for the 2nd- and 1st-order models) is 0.23 for I, 0.16 for V, and 0.18 for O neurons. Although it is not shown here, the binaural term (i. e., the sixth term of Equation 1) has a negligible impact on the quality of the prediction. In 225/310 cases, the improvement in *fv* resulting from the addition of binaural weights was less than 0.01.

Generally the accuracy of model predictions declines as sound level increases. **Figure 8A** shows the relationship between prediction accuracy (*fv)* and RSS stimulus level. The weight-function model most accurately predicts responses of type I neurons (gray lines), and this prediction performance declines slightly with sound level (*R* = −0*.*17, NS). Prediction performance for type V and type O neurons is relatively less accurate across sound

**FIGURE 7 | Quality of the model predictions as measured by** *fv* **in leave-one-out cross validation tests.** Each plot contains an aggregation of prediction data obtained at different sound levels. A neuron is usually represented multiple times, once at each sound level and also at pairs of adjacent sound levels. **(A)** Distribution (left) and cumulative distribution (right) of *fv-*values for the three response types. Vertical lines at the top of the plots show median *fv*-values. Differences in the distributions across the neuron types are statistically significant (rank sum test with Bonferroni correction; I vs. V and I vs. O, *P <<* 0*.*001; V vs. O, *P <* 0*.*02). **(B)** Comparison of the best prediction quality (*fv*) for a full binaural model containing all 2nd-order terms (abscissa) and a model containing only 1st-order terms (ordinate). Ten data points with ordinate values *<* −0*.*2 are not shown, but are included in the statistics. Vertical arrows indicate median *fv*-values for each of the neuron types.

levels (red and blue lines), suggesting that nonlinearities not captured by the weight-function model have a marked impact on the rate responses in these neurons. The decline in prediction accuracy with sound level is significant in V neurons (*R* = −0*.*5, *P <* 0*.*001), but not in O neurons (*R* = −0*.*04, NS).

An important test of a model is whether it can predict responses to stimulus types different from those used to estimate model parameters. For this purpose, we tested the model with a functionally relevant stimulus set—specifically, broadband noise filtered by HRTFs (**Figure 1D**) that simulate natural sound localization cues. As described in Methods, the absence of a reference stimulus for the estimation of *R*0 required a modified prediction approach. In **Figure 8B**, VS response predictions computed in this manner are shown for type I and type O neurons. Whereas rate fluctuations are qualitatively similar between the actual (green lines) and model (blue lines) rates, there are errors in average rate, especially in the type O example (**Figure 8B**). To deemphasize

**FIGURE 8 | Prediction quality for RSS and HRTF stimuli by sound level. (A)** Quality of RSS response predictions (*fv*) plotted against the sound level of the stimulus. Light colored symbols connected by lines show data obtained from one neuron. Heavy colored lines show median *fv*-values for the 3 neuron types. For clarity in plotting, 1 dB was added to the attenuations for V neurons, and 2 dB was added to the attenuations for O neurons. **(B)** Predicted rate responses to HRTF-filtered noise (VS stimuli), computed using weight-function models derived at the same overall sound level (−40 dB). Predictions for a type I (left) and a type O (right) neuron were computed using the difference method (see text). Actual rates are shown in green and predicted rates are shown in blue. The goodness of fit for these predictions are as follows: Type I (left): *R*2 = 0*.*82, *fv* = 0*.*7; type O (right): *R*2 = 0*.*39, *fv* = 0. **(C)** Median *R*2 for the three neuron types. Solid lines indicate median *R*2-values obtained when the full model was used to predict responses to VS stimuli; dashed lines indicate median *R*2-values based only on 1st-order terms. The black dots show the positions of the two examples in **(B)**. The gray shaded region indicates the range of *R*2-values that were observed for type I neurons at each attenuation level.

differences in the means of the actual and predicted rates, the Pearson correlation coefficient *R*2—instead of *fv*—was used to quantify the model fit (see Materials and Methods). **Figure 8C** shows median values of *R*2 for the three neurons types. Consistent with **Figure 7**, the fits are best for type I neurons (gray lines), which maintain good fits (median *R*2 *>* 0*.*5) over the range of sound levels; however, the range of *R*2-values is quite wide, especially at high sound levels (gray shaded region). At each sound level, *R*2-values for type V and type O neurons were comparable to those of type I neurons with the poorest fits.

HRTF response predictions, unlike RSS response predictions, were generally not improved with the addition of 2nd-order terms. In fact, at high sound levels, median *R*2-values were slightly better for 1st-order models, as shown by the dashed lines in **Figure 8C**.

#### **IMPORTANCE OF THE CONTRALATERAL EAR**

Although it is well known that neurons in the CNIC have tuned responses to both contralateral and ipsilateral stimuli, the importance of ipsilateral inputs to spectral selectivity of neurons has not been studied extensively. We investigated this question by fitting a contralateral-only model to the data—that is, a model incorporating only the first, second, and fourth terms of Equation 1. We then used the monaural model to predict responses to test stimuli and compared the quality of those predictions to the quality achieved using the full binaural model (**Figure 9A**). The exclusion of ipsilateral information from the model was found to reduce the quality of the predictions: most of the data points in **Figure 9A** lie below and to the right of the diagonal line. However, the impact of the ipsilateral contribution is relatively small, as inclusion of ipsilateral weights in the model improved *fv* by more than 0.2 for only about 10% of the data.

Ipsilateral weights have a relatively weak impact on the accuracy of the model, partly because they are smaller in magnitude than contralateral weights. This difference in magnitude which is evident in **Figures 2B**, **3B**, **4B**—are summarized in **Figure 9B**. Here, amplitudes of 1st-order contralateral weight vectors (abscissa) are plotted against amplitudes of 1st-order ipsilateral weight vectors (ordinate). These amplitudes are computed as the norm (or length) of the corresponding weight vector. Only type V neurons frequently exhibit ipsilateral norms that are larger than contralateral norms. By contrast, the ipsilateral norms for type I and type O neurons are usually relatively small. In **Figure 9B**, the dashed line indicates where ipsilateral norms are one-fourth the size of contralateral norms. For type I and type O neurons, median contralateral and ipsilateral norms (for contralateral norms greater than 0.6) are roughly approximated by this line. The norms of 2nd-order weights behave similarly (not shown).

#### **EFFECT OF SOUND LEVEL ON SPECTRAL REPRESENTATIONS**

A neuron that provides a robust rate-based spectral representation should respond over as wide a range of discharge rates as possible in response to changing spectral features. The neuron should also adapt the range of response rates to changing stimulus level (Rees and Palmer, 1988; Dean et al., 2008). Each column of **Figure 10A** shows rate responses of a type I neuron to the RSS

stimulus set at two sound levels. The neuron in the left column responds at both sound levels with the full range of rates available to the neuron at the given sound level. By contrast, the neuron in the right column responds at the higher level over roughly half its full rate range due to rate saturation. To evaluate differences in the range of rate responses across sound level, we use the fractional rate ratio (*FRR*), which is defined as ([Rate at 97.5 percentile] – [Rate at 2.5 percentile])/[Rate at 97.5 percentile]. Here, the percentiles (horizontal dashed lines in **Figure 10A**) are used to reduce the effect of occasional rate outliers. *FRRs* for these examples are 0.97 and 0.98 in the left column and 1.0 and 0.50 in the right column. *FRR* is normalized by the maximum rate at a particular level—not the maximum rate across all levels. The latter would produce an increase in *FRR* at low levels that reflects the strength of the response rather than the neuron's use of the range of rates available to it.

As shown in **Figure 10B**, the dynamic range of rate responses are well-maintained in CNIC neurons across sound levels. Here, *FRR* is plotted against sound level for individual neurons (symbols), and heavy colored lines indicate the median *FRR*s for each neuron type. Median *FRR*s are also shown for low, medium, and high spontaneous rate ANFs (dashed lines) responding to the same stimulus set.

In type I and type V neurons, the largest *FRRs* occur at low sound levels and decline monotonically at higher levels. The *FRRs*, which tend to be larger for type I than for type V neurons, are generally comparable to those of low and medium spontaneous rate ANF. Type O neurons have the opposite behavior, showing higher *FRRs* at high sound levels. FRR values across the three groups are statistically different at the levels between −30 and 0 dB attenuation (*P <* 0*.*001 ranksum with Bonferroni correction).

When normalized by their peak value, median contralateral weight vector norms (i. e., amplitudes) of type I, type V, and type O neurons similarly increase at low sound levels and gradually peak between 40 and 30 dB attenuation (**Figure 10C**; solid colored lines). In a comparison of these amplitudes with those of ANFs (black dashed lines), median amplitudes of type I and O neurons correspond closely with those of high SR fibers at low sound levels. This finding suggests that high SR fibers alone can account for the responses of CNIC neurons to RSS stimuli at low sound levels., However, median amplitudes of high SR fibers decrease rapidly above 50 dB attenuation (presumably as a consequence of rate saturation; Young and Calhoun, 2005) whereas CNIC amplitudes continue to peak. The relationship between weight vector amplitudes and attenuation level at these higher sound levels more closely resembles that of low- and medium-SR fibers. Thus, weight vector amplitudes of CNIC neurons seem to reflect the behavior of the most responsive groups of AN fibers at each sound level.

### **DISCUSSION**

#### **THE WEIGHT-FUNCTION MODEL**

Unlike STRFs, weight-function maps computed in this study do not describe the temporal selectivity of CNIC neurons. Rather, weight functions can be viewed as a frequency marginal of the STRF, providing an average of STRFs across the time dimension (Kim and Young, 1994). In neurons where STRFs are separable that is, where STRFs can be written as the product of a frequency function, *A(f)*, and time function, *B(t)*, [i. e., *A(f)B(t)*]—weight functions can be viewed as similar to the frequency function *A(f)*. At lower sound levels, STRFs of most CNIC neurons have been found to be separable (Qiu et al., 2003; Lesica and Grothe, 2008a), but as stimulus levels increase, STRFs lose separability (Lesica and Grothe, 2008a). This observation may mirror the complex shape changes observed in RSS-derived weight functions, particularly among type V and O neurons. A clear example of neurons with inseparable characteristics that can't be modeled

**FIGURE 10 | Relationships between rate dynamic range, weight amplitude, and stimulus level. (A)** Rate responses of two type I neurons to RSS stimuli. Each column contains data from one neuron, presented at two sound levels, as given by the legends. Horizontal dashed lines indicate rates at the 2.5 and 97.5 percentiles of the distribution of rates. *FRR* is calculated from these rates, see text. **(B)** *FRR*s of type I, O, and V populations at different attenuations of the RSS stimuli. Symbols mark the *FRR* of one neuron's responses at the indicated sound level. Abscissa positions of the data points are dithered by ±3 dB to improve clarity. Solid colored lines are the median *FRRs* for the neuron types in 10 dB bins. *(Continued)*

#### **FIGURE 10 | Continued**

Black dashed lines are the median *FRR*s of 355 ANFs with high spontaneous rate (*Hi SR*) and low-to-medium spontaneous rate (*LM SR*) [from Young and Calhoun (2005)]. Low and medium SR fibers have been combined because they exhibit identical behavior. **(C)** Median norms of 1st-order weight vectors for CNIC neurons (contralateral only; colored lines) and ANFs [dashed lines; from Young and Calhoun (2005)]. For each neuron type, median norms have been scaled to a maximum value of 1 for comparison. Median norms peak at the following values, in spikes/s/dB: type I, 2.4; type V, 1.4; type O, 1.4; low-SR ANF, 3.0; medium-SR ANF, 3.0; and high-SR ANF, 2.9.

with weight functions are those with directional responses to frequency sweeps (Andoni and Pollak, 2011). In addition, a strictly spectral model is not useful in describing responses to stimuli with substantial amplitude or frequency modulation (Delgutte et al., 1998; Lesica and Grothe, 2008b; Zheng and Escabi, 2008).

Despite these limitations, the weight-function model has advantages over other approaches to broadband characterization of auditory neurons. One is that it is easy to express the responses in terms of nonlinear stimulus dimensions that are natural for the auditory system, i.e., log frequency and log sound level. The appropriateness of log frequency is clear from the layout of frequencies on the basilar membrane and in central auditory maps. The appropriateness of log sound level is suggested by amplitude compression in the response of the basilar membrane [reviewed by Robles and Ruggero (2001)] and by the fact that discharge rate is locally linear with dB amplitude in AN fibers (Sachs et al., 2006) and neurons in the cochlear nucleus (May et al., 1998). Further evidence is the fact that computing STRFs with a logarithmic stimulus-amplitude scale results in better fits in prediction tests compared to a linear or power scale (Escabi et al., 2003; Gill et al., 2008).

The actual functional form of the best-fitting input nonlinearity was determined from data by Ahrens et al. (2008) in auditory cortex neurons. Their input nonlinearities were expressed as functions on a log stimulus amplitude scale, i.e., as an additional nonlinearity beyond the logarithm. In most cases, the functions were linear over some portion of the log scale, consistent with the model used here, but modified by rectification and sometimes saturation at low and high levels. Another approach to describing input nonlinearities, which was mentioned in the introduction (Bandyopadhyay et al., 2007), is to incorporate an intensity nonlinearity into a 1st-order weight function for neurons in dorsal cochlear nucleus. In this case, the nonlinearity is frequency-dependent and cannot be represented with a single input nonlinearity.

#### **IMPORTANCE OF SECOND-ORDER FILTERS**

Previous studies of CNIC neurons have yielded conflicting perspectives on the importance of 2nd-order filters. In the bat, 2nd-order filters have been shown to be important in describing CNIC neuron responses (Andoni and Pollak, 2011); by contrast, in the cat, 2nd-order filters have been found not to be useful (Atencio et al., 2012). Reasons for these differences are not clear. However, the results of the current study clearly support the view that 2nd-order filters are important in the CNIC. As shown in **Figure 7B**, the accuracy of the spectral integration model for CNIC neurons is improved with the addition of 2nd-order terms. In fact, in 93% of neurons studied, the addition of one or two judiciously chosen 2nd-order weights increased *fv*-values to levels not achievable through the addition of 1st-order weights alone (data not shown). The latter observation indicates that improvements in prediction accuracy depend specifically on 2nd-order terms and do not merely reflect the inclusion of a larger number of model parameters. Similar improvements to 1st-order models achieved through the inclusion of 2nd-order terms have been described for temporal responses in auditory cortex (Pienkowski et al., 2009).

The improvement in prediction performance achieved with 2nd-order filters may simply reflect the addition of static nonlinearities that match the curvature of a neuron's rate-level function. In models based on the STRF, prediction quality is often improved by following the linear STRF with a static nonlinear function one that matches the amplitude of the STRF output to the neuron's response rate (e.g., Escabi et al., 2005; Nagel and Doupe, 2006; Lesica and Grothe, 2008a; Sharpee et al., 2008; Atencio et al., 2012). In these so-called "linear-nonlinear models," the nonlinear segment often bears the shape of a parabola that is dominated by a 2nd-order term. In the current study, the 2nd-order filters are equivalent to such models insofar as the 2nd-order filter and 1st-order weight functions demonstrate the same frequency selectivity. This was sometimes observed in the CNIC, as with the type O neuron in **Figure 3D** (blue filter, bottom left). However, in almost all cases, 2nd-order filters exhibited frequency selectivity that differed significantly from that of 1st-order weight functions (see part **D** of **Figures 2** through **4**). This suggests that a response function in the CNIC often cannot be described by simply appending a single nonlinear filter to a linear one. This finding has important functional implications, as it reveals a complexity in the frequency selectivity of CNIC neurons that is not captured by a 1st-order model alone.

However, the 2nd-order terms in weight functions are not universally useful, as was found for responses to HRTF filtered noise (**Figure 8C**). Although the addition of 2nd-order terms improved RSS response predictions, this was not true for HRTF response predictions. In fact, on average, weight-function models predicted HRTF responses better without the 2nd-order terms. Errors resulting from the inclusion of 2nd-order terms may reflect inaccuracies in modeling the average rate responses to those stimuli. When 2nd-order terms were added, errors in average rate were found to be larger than gains in the rate fluctuations. This suggests that the estimation of 2nd-order terms is more stimulusdependent than the estimation of 1st-order terms. This interpretation is consistent with the general finding that STRF models perform better for the stimulus type to which they are fit (e.g., Theunissen et al., 2000; Machens et al., 2004). A similar result was recently obtained while trying to predict the binaural responses of neurons in the nucleus of the brachium of the inferior colliculus (Slee and Young, 2013). Here, the responses to binaural VS were well-predicted by an RSS model with only1st-order terms.

#### **BINAURAL INTERACTION**

As in previous studies of decerebrate cats (Davis et al., 1999; Chase and Young, 2005), CNIC neurons in the present sample were found to be binaural: that is, monaural stimuli presented to either ear yielded a response with a clear BF. Frequency selectivity in binaural CNIC neurons is often studied using monaural stimuli, where stimuli are presented either to the contralateral ear alone or in free field. But auditory neurons rarely encounter stimuli that are truly monaural in a natural environment. Studies involving binaural free-field stimulation, which are often based on a more natural stimulus presentation, may also be problematic in that they usually do not account for the varying dichotic nature of the stimuli. As such, responses dependent on the interaction of contralateral and ipsilateral inputs—e. g., sensitivity of neurons to interaural level differences (Delgutte et al., 1999)—are often not analyzed in a systematic manner that is frequency-specific.

In this work, independent RSS stimuli were presented to the two ears simultaneously. This binaural stimulus presentation enabled interactions between contralateral and ipsilateral inputs—as well as contralateral and ipsilateral frequency selectivity—to be investigated in a controlled manner. Furthermore, frequency selectivity, which was computed in the form of weights, could be compared between the two ears. For type I and O neurons, contralateral weights were almost always found to be larger in magnitude than ipsilateral weights (**Figure 9B**), suggesting that the responses of these neurons are more strongly influenced by spectra presented to the contralateral ear. Studies of CNIC neurons based on STRFs corroborate this finding, indicating that ipsilateral STRFs—which were found to be significant for only 36% of CNIC neurons studied—usually provide weak spectral representations (Qiu et al., 2003). For some binaural CNIC neurons, the contralateral weight functions alone appear sufficient to describe rate responses (e. g., the points on the diagonal line in **Figure 9A**), suggesting that these neurons are weakly responsive to ipsilateral RSS inputs. Prediction testing does suggest, however, that inputs from the ipsilateral ear contribute significantly to the responses of many CNIC neurons. **Figure 9A** indicates that spectral integration models that ignore inputs to the ipsilateral ear may yield errors in a large number of neurons. Difficulties reported in using STRFs to predict CNIC responses may reflect, to some extent, omission of ipsilateral inputs.

The consistently small size of binaural interaction weights (i. e., *bjk*, the sixth term in Equation 1) suggests that binaural interactions, as they pertain to stimulus spectra (i. e., interaural level differences), are primarily additive rather than multiplicative. This conclusion is subject to the reservation that binaural interactions related to interaural time differences are not addressed here.

#### **LEVEL-TOLERANT TUNING**

The tuning of response areas—whether derived from noise or tone stimuli—becomes more level tolerant across the ascending pathway from the cochlea to the auditory cortex. For tones, neurons show sharper tuning in CNIC than in the cochlea (measured by Q40; Ramachandran et al., 1999; McLaughlin et al., 2007), and fully level-tolerant tone response maps have only been reported in auditory cortex (Sutter, 2000; Sadagopan and Wang, 2008). Tuning to broadband stimuli is more level tolerant than tuning to tones, even at the level of the auditory nerve (Carney and Yin, 1988; Young and Calhoun, 2005) where level tolerance is believed to reflect cochlear suppression. In this study, we show weight-function maps that are more level tolerant than auditory nerve maps (**Figure 6B**), and tuning in type I and O neurons that is fully level tolerant. Experiments based on antagonists to inhibitory neurotransmitters (Yang et al., 1992; Lebeau et al., 2001), small cochlear lesions (Snyder and Sinex, 2002), and stimulus-driven adaptation suggest that sharpening of tone tuning can be attributed to inhibitory inputs at frequencies away from BF. The level tolerance observed in CNIC weighting functions is likely also shaped by this inhibition. From a functional perspective, level tolerance is significant: it suggests that CNIC neurons are capable of maintaining selectivity for narrow spectral features—such as those found in HRTFs—across a wide range of stimulus levels.

#### **RESPONSE TYPES IN THE CNIC**

The results shown here add to the current understanding of neural response types in the cat CNIC. The classification scheme applied in this study is based on spectral tuning for tones (Ramachandran et al., 1999; Greene et al., 2010), analysis of masking patterns (Ramachandran et al., 2000), and binaural processing (Davis et al., 1999; Ramachandran and May, 2002). The validity of this scheme is supported by the fact that class definitions incorporate multiple aspects of a neuron's response properties. Moreover, similar response types have been described in a number of animal species (when effects of anesthesia and stimulus design are taken into account; for example: bat, Yang et al., 1992; guinea pig, Lebeau et al., 2001; mouse, Egorova et al., 2001; rat, Hernández et al., 2005). The relative prevalence of the three response types does varies widely across species (Davis, 2005). Neurons resembling types I and V are found in all species, but the prevalence of type O neurons, which are most common in cats, varies significantly. This suggests that there are significant species differences in the representation of sound in IC.

In contrast to tone maps, the shapes of weight-function maps as described in this study do not clearly define the response types nor allow them to be differentiated. In type I neurons, weightfunction maps usually exhibit on-BF positive (excitatory) peaks for the contralateral ear and on-BF negative troughs (inhibition) for the ipsilateral ear (as in **Figure 2**). It can also be stated that in type O and type V neurons, contralateral and ipsilateral weightfunction maps usually contain areas of inhibition near BF that are more prominent than those for type I neurons (as in **Figures 3**, **4**). However, patterns of excitation and inhibition observed in weight-function maps—within a single response type—vary significantly, particularly within the type V and O classes. As a

#### **REFERENCES**

Adams, J. C. (1979). Ascending projections to the inferior colliculus. *J. Comp. Neurol.* 183, 519–538.

result, weight-function maps do not in themselves support a welldefined classification scheme. Likewise, studies of STRFs have yet to define a classification scheme for auditory neurons in CNIC or elsewhere [although see Woolley et al. (2009) in bird cortex].

On the other hand, results of this study do support the functional relevance of the type I, V, and O classification scheme. Specifically, the nature of spectral sensitivity differs for the three classes. As demonstrated in **Figures 7**, **8**, each of the neuron types is sensitive to spectral shape, as rate responses vary with changes in stimulus spectrum. For type I neurons, a low-order model containing only 1st- and 2nd-order terms typically accounts for most of the variance in rate responses—and thus spectral sensitivity at stimulus levels up to 50–60 dB above threshold. By comparison, a low-order model is a less accurate description of type V and O rate responses, except at the lowest stimulus levels (**Figure 8**). The nature of spectral encoding therefore appears to be different for type I, V, and O neurons.

Differences in spectral encoding between type I, V, and O neurons support the idea of parallel representations of auditory stimuli (Yu and Young, 2000; Escabi and Schreiner, 2002; Woolley et al., 2009). Type I neurons, like chopper neurons in the ventral cochlear nucleus, produce linear representations of spectral shape. The weight function model did successfully capture the variation in rate responses across a set of untrained, functionally relevant VS stimuli (see **Figures 8B,C**). On the other hand, type O and V neurons exhibit nonlinear relationships between spectral level and discharge rate that imply other functional possibilities. One possibility is that "nonlinear" neurons may be encoding specific features of the stimulus—e. g., spectral notches or rising spectral edges, as are present in VS stimuli—in a manner not easily explained with a linear spectral integration model. Such neurons have been observed in the dorsal cochlear nucleus and CNIC (Davis et al., 2003; Escabi et al., 2005; Reiss and Young, 2005). Alternatively, the responses of nonlinear neurons may be more strongly influenced by properties of the stimulus other than the spectra, such as features encoded in the time domain. For example, type V neurons seem to be specialized for encoding interaural time differences (Ramachandran and May, 2002; Chase and Young, 2005), which were not controlled in RSS stimuli presentations. A full exploration of the link between response nonlinearity, feature selectivity, and temporal sensitivity remains to be done.

#### **ACKNOWLEDGMENTS**

Supported by grant DC-000115 from NIDCD. The technical assistance of Ron Atkinson, Phyllis Taylor, and Qian Li facilitated this work. Data acquisition software was written by Alon Fishbach.

a neural computation for encoding natural communication signals in bat inferior colliculus. *J. Neurosci.* 31, 16529–16540.

spectrum stimuli. *J. Neurosci.* 23, 7194–7206.

sound? Auditory area CLM encodes stimulus surprise, not intensity or intensity changes. *J. Neurophysiol.* 99, 2809–2820.

complex sounds. *J. Neurophysiol.* 106, 1016–1027.

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 28 January 2013; accepted: 22 April 2013; published online: 10 May 2013.*

*Citation: Yu JJ and Young ED (2013) Frequency response areas in the inferior colliculus: nonlinearity and binaural interaction. Front. Neural Circuits 7:90. doi: 10.3389/fncir.2013.00090*

*Copyright © 2013 Yu and Young. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any thirdparty graphics etc.*

## Distribution of visual and saccade related information in the monkey inferior colliculus

#### *David A. Bulkin1 \* and Jennifer M. Groh2,3,4*

*1 Department of Psychology, Cornell University, Ithaca, NY, USA*

*3 Department of Psychology and Neuroscience, Duke University, Durham, NC, USA*

*4 Center for Cognitive Neuroscience, Duke University, Durham, NC, USA*

#### *Edited by:*

*Manuel S. Malmierca, University of Salamanca, Spain*

#### *Reviewed by:*

*Yang Dan, University of California, Berkeley, USA Christopher I. Petkov, Newcastle University, UK*

#### *\*Correspondence:*

*David A. Bulkin, Department of Psychology, Cornell University, 243 Uris Hall, Ithaca, NY 14853, USA. e-mail<EMAIL_ADDRESS>
The inferior colliculus (IC) is an essential stop early in the ascending auditory pathway. Though normally thought of as a predominantly auditory structure, recent work has uncovered a variety of non-auditory influences on firing rate in the IC. Here, we map the location within the IC of neurons that respond to the onset of a fixation-guiding visual stimulus. Visual/visuomotor associated activity was found throughout the IC (overall, 84 of 199 sites tested or 42%), but with a far reduced prevalence and strength along recording penetrations passing through the tonotopically organized region of the IC, putatively the central nucleus (11 of 42 sites tested, or 26%). These results suggest that visual information has only a weak effect on early auditory processing in core regions, but more strongly targets the modulatory shell regions of the IC.

**Keywords: multisensory, auditory, vision, cross-modal**

### **INTRODUCTION**

Recent work has implicated the inferior colliculus (IC) in audiovisual integration. IC neurons can show different responses to auditory stimuli when presented with concurrent visual stimuli, and can even exhibit overt responses in the absence of a sound (Mascetti and Strozzi, 1988; Gutfreund et al., 2002; Porter et al., 2007; Bergan and Knudsen, 2009) (for review, see Gruters and Groh, this issue). The IC is a critical site of convergence along the ascending auditory pathway: virtually all auditory signals from the brainstem must stop in the IC before reaching thalamus and eventually cortex (Aitkin and Phillips, 1984; Winer and Schreiner, 2005). Thus, visual effects in the IC have the potential to exert a powerful influence on subsequent auditory processing. However, the subnuclei of the IC have dramatically different connectivity patterns. In order to interpret the implications of visually sensitive neurons in the IC it is essential to localize them within the structure.

Recently, our group demonstrated visually sensitive neurons in the macaque IC during a visual fixation task (Porter et al., 2007). A large proportion of sampled units (64%) showed changes in activity that were time-locked to either the onset of a visual fixation cue and/or eye movements that followed. However, because recording location was not systematically varied, it has remained unclear whether these responses are found throughout the IC or are confined to a subregion. In owls, visual modulation of activity has been localized within the lateral nucleus (Gutfreund et al., 2002; Bergan and Knudsen, 2009), but comparable data for other species are sparse and the possibility remains that other regions also show visual sensitivity.

Identifying the precise location of recordings by combining physiology and histology is particularly challenging in large animals such as, macaques. Physiological data are collected over several months, with many repeated electrode penetrations, and individual animals often participate in several experiments. Thus, by the time anatomical data are collected and analyzed, the location at which individual units were recorded is unclear. If only a few histologically-confirmed recording sites can be established for each animal, the time, effort, and cost involved in accumulating a sufficient sample is prohibitive.

One alternative to direct anatomical methods is to utilize known topographic patterns to form a functional map. This approach provides an indirect estimate of location, but allows measures that are centered on the recording electrode specific to each recording penetration. In this manner, the location of neurons possessing a particular functional feature (i.e., sensitivity to visual and visuomotor events) can be assessed using a previously ascertained organization (i.e., functional auditory topography).

We recently defined such a functional map by systematically sampling auditory responses in macaques (Bulkin and Groh, 2011). We measured multiunit neuronal activity throughout the midbrain of six macaques while presenting sounds, allowing delineation of several subregions of the macaque IC consisting of an area showing a tonotopic organization, a surrounding area with neurons tuned for low frequency sounds (tuned area), and a peripheral area in which neurons were either not tuned for tone frequency or not responsive to tones (untuned area). Using this functional map, we presently identify the locations of visually responsive neurons.

Overall, 84 of 199 (∼42%) sampled locations showed a change in firing rate following the onset of the fixation stimulus. Responses were most common and most powerful in recordings taken along untuned penetrations, located chiefly in the rostral portion of our recording zone. In penetrations passing through the low frequency tuned, non-tonotopically organized, region of the IC, fewer (and weaker) responses were found. Along tonotopic penetrations, we found only subtle (though significant) effects

*2 Department of Neurobiology, Duke University, Durham, NC, USA*

of visual stimulus presentations. Despite the lack of robust spiking responses in the tonotopically organized region of the IC, evoked potentials from this area were on par with recordings from neighboring subregions.

These results exhibit similarities and differences compared to our recent findings concerning the distribution of eye-position sensitivity in the IC (Bulkin and Groh, 2012). Neurons sensitive to the orientation of the eyes were most prevalent in recordings in tuned and untuned penetrations, like we report here for visual sensitivity. However, the amplitude of eye position modulation was similar throughout all regions of the IC, whereas we report here that modulation of neuronal activity in response to visual stimuli was much weaker along tonotopic penetrations, putatively the central nucleus of the IC, than in other areas. Taken together, the pattern of overt visual responses and eye position sensitivity supports the view that these multisensory influences are generally more powerful in the tuned and untuned areas, where neuronal activity was strongly related to both the position of the eyes and presentation of visual stimuli, than in the tonotopic area. The dissociation of these two effects in the tonotopically organized IC suggests these two signals may play somewhat different roles in the core ascending auditory pathway.

## **MATERIALS AND METHODS**

#### **SURGICAL PREPARATION, RECORDING PROCEDURES, AND INCLUSION CRITERIA**

Three male and three female rhesus monkeys participated in the experiments. All procedures were approved by the Institutional Animal Care and Use Committees at Dartmouth College and Duke University, and were conducted in accordance with the principles of laboratory animal care of the National Institutes of Health (publication 86-23, revised 1985). Surgical procedures were performed using isoflurane anesthesia and aseptic techniques as well as postoperative analgesia.  After recovery, an additional surgery was performed to make a craniotomy and to implant a recording cylinder positioned to allow electrodes to approach the left IC at an angle approximately 30◦ from vertical in the coronal plane. The chamber contained a fixed grid of holes (Crist Instruments, Gaithersburg, MD) aligned such that electrode penetrations could be made in 1 mm increments in the anterior/posterior and (tilted) medial/lateral dimensions.

Recordings were made using tungsten microelectrodes (1–3 M*-*; FHC Inc., Bowdoin, ME). Multiunit clusters were selected using a window discriminator (Monkeys A,W: Plexon Inc., Dallas, TX; Monkeys E,M: Bak Electronics, Germantown, MD) and times of action potentials were stored for off-line analysis. A separate local field potential (LFP) signal (band pass filtered between 0.7 and 300 Hz) was collected and digitized (sampling rate 20 KHz). The location of the IC was determined using an anatomical MRI scan in which the recording chamber and plastic grid could be visualized, and was verified using physiological responses. Using MRI, we estimated all of the borders of the IC except on the rostral aspect, where a clear definition was not visible. This study was part of a larger mapping study in which we sampled neuronal activity throughout the region (in 1 mm increments in the transverse plane and 0.5 mm increments along the depth axis of our recording penetrations) to form a functional map of the region (Bulkin and Groh, 2011). Recordings to assess visual sensitivity were taken at a subset of locations, limited by the behavioral performance of the monkeys, which was required for the visual assessment but not for auditory assessment. Recording sessions began and ended at depths above and below the putative IC to ensure that the entire structure was covered. Analysis of visual response data was limited to locations within the borders measured by MRI scans ±1.5 mm, and meeting auditory response criteria. Sites were included if responses to sounds exceeded 3 SDs of baseline for 10 ms consecutively in a 50 ms window following auditory stimulus onset, and at least three sites within a penetration had to show such responses.

We tested a subset of sites, notably those in the rostral-most penetrations in monkey A, with microstimulation to rule out that they were in the superior colliculus (SC). The SC is an oculomotor structure rostral and dorsal to the IC, and it exhibits auditory responsiveness when animals are engaged in auditory saccade tasks (Jay and Sparks, 1984; Populin et al., 2004). Saccades can be elicited by stimulation in the SC (Robinson, 1972) but are not expected to be elicited from the IC. The absence of stimulationevoked saccades can therefore help confirm that the MRI-based and physiological inclusion criteria described above are sufficient to limit auditory-responsive sites to those in the IC. Saccades were only elicited at 2 of 51 tested sites included in the auditory data set, confirming the adequacy of anatomical and physiological criteria. Neither of these sites was probed for visual responses, so they were not analyzed further for the present study. In more dorsal, also non-included locations, saccades were frequently observed following stimulation, with a predictable topographic organization of evoked saccade vectors. Such sites were probably in the SC.

#### **STIMULUS PRESENTATION AND BEHAVIORAL TASK**

Experiments were conducted in complete darkness in a singlewalled sound isolation booth (International Acoustics Company, New York, NY). Echo-absorbent material lined the walls and ceiling (3 inches, Sonex Painted One acoustic foam), as well as the floor (carpet). The head of the monkey was restrained throughout the experiment. Visual stimuli consisted of light emitting diodes (LEDs; luminance ∼26.4 cd/m2) presented from an array located ∼57 inches in front of the head.

In designing a task for this study, we were motivated by several considerations. The previous finding of visual responses in the monkey IC was a somewhat opportunistic one: visual responses were observed in conjunction with the onset of, or saccade to, a visual fixation target in a task in which visual fixation was followed by sound presentations (Porter et al., 2007). We wanted the task in the current study to also involve a visual fixation stimulus, and a similar time course, so that we would be able to make direct comparisons with the previous study. The task used previously included the presentation of a sound. This auditory stimulus was presented after the periods probed for visual-related responses, but we could not rule out the possibility that anticipation of the sound contributed to visual responsiveness. Here, we wanted to eliminate any chance that a temporally-linked auditory stimulus might be connected to the observed visual effects. Thus, in the present experiments we used tasks that contained no auditory stimuli.

Monkeys were trained using operant conditioning to fixate visual stimuli for a fluid reward. At each recording site approximately 200 rewarded trials were collected. Two task designs were used and are depicted in **Figure 1**. Both tasks began with the fixation of an LED presented at eye level. Subsequently an additional "probe" LED was briefly flashed at 1 of 12 locations. Monkeys E and M were trained to make an additional saccade to the probe stimulus following the offset of the initial fixation LED (**Figure 1A**). Monkeys A and W were trained to ignore the probe LED and maintain fixation on the initial LED (**Figure 1B**). To allow comparison with our previous study and across monkeys within this study, all of the neurophysiological responses reported in this paper derive from epochs associated with the onset of this fixation target and its associated saccade (the shaded regions in **Figure 1**), i.e., when the task was similar across subjects and studies. If the monkey failed to fixate the appropriate target, or

**FIGURE 1 | Fixation tasks used to probe for visual responses.** This figure illustrates the behavioral tasks used to probe for visual stimuli. Monkeys E and M performed a multiple fixation task **(A)** whereas monkeys A and W held fixation while ignoring a probe stimulus **(B)**. Temporal intervals tested for visual modulation of activity **(C)** used only the first part of the trials, that was common across monkeys. The tested epochs indicated in **(C)** are not drawn to scale as trials involved variable time intervals.

broke fixation, the trial was unrewarded and not included in the analysis. A variable interval ranging from 1 to 2 s followed the reward or error during which no stimuli were presented.

Acoustic stimuli used to ascertain tuning characteristics were presented in separate blocks at the sites probed for visual responses, as well as at numerous sites not tested for visual responsiveness. Auditory stimuli were presented on unrewarded trials with no other stimuli or performance requirements, and consisted of tones of 16 frequencies ranging from 0.4 to 12 kHz (approximately ¼ octave increments), as well as broadband noise (spectrum ranging from 0.5 to 18 kHz). Sounds were generally presented using loudspeakers (Audax Model TWO25V2 or Bose Acoustimas cube speakers) located 90◦ contralateral to the recording chamber and 57 in. from the subject's head. In all monkeys sounds were presented at 50 dB SPL, calibrated to within 1 dB of the target amplitude using a sound meter (Brüel & Kjær, model 2237 with model 4137 condenser microphone; A-weighted) placed at the position that the monkey's head would occupy in the experiment. The neural responses to sounds were used to classify recordings in a map of IC auditory topography.

#### **DATA ANALYSIS**

Data were analyzed off-line to determine which sites along a penetration showed auditory responses. Auditory penetrations (showing three or more responsive sites) were classified as tonotopic if they showed a systematic progression of increasing tuned frequency with depth, tuned if three or more recordings within a penetration showed frequency tuning (but no systematic progression of tuned frequency), or untuned if the sites either did not respond to tones or did but responded similarly for all tone frequencies. The methods for the auditory response analysis have been described previously (Bulkin and Groh, 2011).

To determine if visual stimuli elicited a response, we compared firing rates during a baseline period (200 ms preceding stimulus onset) to the firing rate during four response epochs: 50–250 ms following the LED illumination, 50–250 ms following the saccade to the LED, 100 ms centered on the saccade, and 100–500 ms following onset of the LED (**Figure 1C**, **Table 2**). We used a two-tailed paired *t*-test to look for significant changes in activity, with a Bonferroni corrected criterion alpha value (i.e., *p <* 0*.*0127). The rationale for probing for responses in these epochs was to detect sites with different response properties. For example, sites with receptive fields in different locations might exhibit responses during different time periods, based on the eye movements required to perform the task. Sites with extra-foveal receptive fields might respond during the initial appearance of the LED if it happened to lie within that receptive field. Sites with foveal receptive fields would be more likely to show a response at the completion of a saccade to the LED, which would bring the LED onto the fovea. Sites with activity coupled to the saccade would show activity during the time window centered on the saccade. And we have previously observed some sites with slowly increasing activity following the onset of the stimulus (Porter et al., 2007).

Occasionally, electrical noise generated by the LED apparatus influenced recording equipment and generated a spurious response. Artifacts were readily detected as increases in activity

following the onset of the LED with no latency. These sites were removed from analysis by probing for increases in activity exceeding two standard deviations of baseline firing during the first 20 ms of following stimulus onset. Seventeen recordings were excluded from analysis due to too-short latency changes in firing rate.

To investigate spatial sensitivity to visual stimuli, we fit a two-dimensional Gaussian surface to the response (in each of the windows used for response detection described above) as a function of the horizontal and vertical location of the retinal or eye-centered stimulus location.

$$f\left(\mathbf{x},\boldsymbol{\nu}\right) = \mathbf{C} + \mathbf{A}\mathbf{e}^{-\left(\frac{\left\|\mathbf{x}-\boldsymbol{\mu}\_{\boldsymbol{\mathcal{X}}}\right\|^{2}}{2\sigma\_{\boldsymbol{\mathcal{X}}}^{2}} + \frac{\left\|\boldsymbol{\nu}-\boldsymbol{\mu}\_{\boldsymbol{\mathcal{Y}}}\right\|^{2}}{2\sigma\_{\boldsymbol{\mathcal{Y}}}^{2}}\right)}$$

*f*(*x*, *y*) is the estimated firing rate response at eye-centered horizontal (*x*) and vertical (*y*) locations of the stimulus at onset. As the eyes were freely moving before the initial fixation cue, there was spontaneous variation in the retinal position of the stimulus. Estimates of μ*x* and μ*y* define the center of the receptive field, and σ*x* and σ*y* the spread of the responsive area in the horizontal and vertical dimensions. The amplitude, A, describes the ratio between responsive and unresponsive regions. We allowed both positive and negative amplitude Gaussian fits, meaning that the center of the surface could represent a peak or trough. To compare receptive fields across the population we calculated the diameters of an ellipse made by the Gaussian at half the height of the peak with respect to the offset:

$$\text{FWHM}\_{\text{x}} = \sigma\_{\text{x}} \cdot 2\sqrt{2\ln 2}$$

$$\text{FWHM}\_{\text{y}} = \sigma\_{\text{y}} \cdot 2\sqrt{2\ln 2}$$

#### **RESULTS**

We tested for visual responses in the activity of multiunit clusters distributed throughout the IC of four rhesus macaques. A total of 84 of 199 recording sites showed a response to the presentation and fixation of an LED in at least one of four tested epochs surrounding the events (**Tables 1**, **2**). Our most extensive sample came from monkey A due to improvements in the techniques we used to target potential recording sites.

**Figure 2** displays example response patterns of multiunit clusters from three recording locations. IC cells show a mix of visual and visuomotor activity (Porter et al., 2007). Objectively classifying responses as related to visual or motor events is impossible in a simple fixation task as the onset of the LED is temporally linked to the onset of the saccade. Further confusing the issue, IC visual responses show spatial sensitivity, so a response temporally aligned with saccades may indicate either motor related activity (if it occurs prior to the saccade) or the repositioning of the visual stimulus on the retina (if it occurs following the saccade). Nonetheless, conjectures about the event best linked to responses can be garnered by investigating raster plots sorted on the time of the saccade. **Figures 2A–C**shows histograms and raster plots aligned on the onset of the LED. Rasters have been sorted on reaction time such that the slowest (i.e., longest duration between LED onset and saccade) responses are shown at the bottom of the panels. The red trace indicates the time the eyes entered the criterion target fixation window. At the top of the raster a number of trials are seen with no response latency; in these trials the monkey's eyes were directed at a point within the criterion window when the LED came on (though a small saccade within the window may have followed).

The examples in this figure show the range of different kinds of response patterns observed in our data. The increase in firing rate in Example 1 (panel A) appears time locked to the onset of the LED. The activity increased following the onset of the LED, and then decreased following the saccade. Whether this subsequent decrease is directly related to the saccade, or to the new position of the stimulus on the retina following the saccade (i.e., the fovea) is unclear. Example 2 (panel B) shows an increase around

**Table 1 | Proportion of visual/visuomotor sensitive sites in functionally defined locations.**

**Table 2 | Proportion of visual/visuomotor sensitive sites in functionally defined locations by response epoch.**

**activity.** Neural spiking data is plotted for each of three example recording sites in the IC. Panels **(A–C)** display PSTHs and rasters aligned on the onset of the LED. The time of the saccade is indicated by red points in the rasters. An increase in firing rate, time-locked to the onset of the LED, can be observed in panels **(A)** and **(C)**. Panels **(D–F)** display the same data, but aligned instead on the time at which the monkey's eyes entered the criterion

motor-related response can be seen in panel **(E)**. Panels **(G–I)** indicate the spatial tuning properties of the responses using spontaneous changes in eye orientation at the time of LED onset. The ordinate and abcissa indicate the location of the fixation stimulus in eye-centered co-ordinates. Color tracks the firing rate (subtracting baseline), warmer colors indicate increases in firing rate, while cooler colors indicate decreases.

the time of the saccade. This increase is much clearer when the same data are aligned on the time the subject's eyes entered the fixation window (panel E). The increase in activity began immediately preceding the saccade, clearly distinguishable from a visual-related response. Nonetheless, the activity remains above baseline following the saccade, perhaps indicating the presence of a foveal receptive field. Example 3 shows increases in firing rate following both the onset of the LED and the fixation response. In both stimulus-aligned (**Figure 2C**) and response-aligned plots (**Figure 2F**), a transient increase in activity is seen following both the onset of the LED and the saccade.

**Table 2** provides the results according to the epochs that indicated significant effects. Generally, sites that showed an increase in activity potentially corresponding to visual-related activity (epochs 1, 2, and 3) also showed a response in the saccade related epoch (epoch 4). (Again, note that although epoch 2 is aligned on the time of the saccade, activity during this temporal window would correspond to a visual effect, with the stimulus occupying a new location on the retina following the saccade.) Because epoch 4 overlaps with epochs 1 and 3, the data cannot unambiguously distinguish visual from motor responses. Nonetheless, virtually all sites marked as responsive in epoch 4 were also marked in one of the other epochs, suggesting a low incidence of purely motor related activity. These results generally resemble our previous findings at the single-unit level (Porter et al., 2007).

The spatial sensitivity of responses was analyzed by relating activity to the location of the LED with respect to the direction of gaze when it was illuminated. **Figures 2G–I** show estimates of spatial tuning for the three examples. Negative numbers on the x-axis indicate locations in the ipsilateral hemifield; negative numbers on the y axis indicate locations below fixation. Color indicates firing rate, from the epoch that best captured the response (G,I: 50–250 ms following LED onset; H: a 100 ms window centered on the saccade). Warmer colors, indicating increases in firing rate relative to baseline, are seen on the right of panel G: this site showed the largest responses to locations in the contralateral hemifield. Example 2 showed a receptive field in the lower half of the plot, this site responded best when stimuli were below spontaneous pre-trial fixation. Example 3 showed a smaller receptive field, with maximal responses when the LED was in the contralateral hemifield and near or above spontaneous pre-trial eye orientation. A similar plot based on responses in a peri-saccade window was virtually identical (data not shown).

#### **ANATOMICAL DISTRIBUTION OF VISUALLY EVOKED CHANGES WITHIN THE IC**

Visually responsive recording sites were not restricted to a small region within the IC. **Figure 3** presents a three-dimensional reconstruction of recording penetrations. Grey vertical lines indicate the trajectory of recordings, which traversed through the midbrain along a dorsolateral to ventromedial course (see Materials and Methods). Along the penetrations, regions showing auditory responses are indicated by colored lines according to the categorical assignment of the penetration's auditory response (Bulkin and Groh, 2011). The locations of visually probed sites are indicated with black circles along the lines, filled markers show a change in firing rate in any of the temporal epochs tested. On occasion, the same location was tested on multiple penetrations. In these cases, a location is marked as visually responsive if any of the recordings showed a response. Responsive sites are found throughout the probed region in monkey A, and no completely unresponsive region is seen. As illustrated in **Table 2**, this was true for each of the different response epochs that we tested. These data indicate that modulation of activity following presentation of visual stimuli is not limited to a small subregion of the IC; rather it spanned the extent of our recordings.

A greater proportion of visually sensitive sites are evident along the peripheral, untuned penetrations in **Figure 3**. Indeed, visual sensitivity was observed at the majority of sites in untuned

**recording sites.** The location of visually sampled sites are portrayed in a three dimensional representation. Auditory responsive regions are indicated with colored lines untuned penetrations: blue lines; tonotopic penetrations: green lines; tuned penetrations: red lines; (Bulkin and Groh, 2011). Sites that were tested for visual responses, but did not show a significant change in firing rate are indicated with open circles, responsive locations are indicated with filled circles. The locations of the recording sites used for examples in **Figure 2** are marked with yellow, cyan, and magenta circles (all were taken from Monkey A).

penetrations, largely found along rostral penetrations in monkey A (**Figure 4**). Importantly, note that these recordings were not taken from the nearby SC. We confirmed the location of the SC was dorsal to our most rostral recordings using microstimulation to probe for evoked saccades (see Materials and Methods for details).

**Figure 5** shows the proportion of responsive sites as a function of recording depth for each of the penetration categories. Penetrations were aligned based on the first identified auditory responsive site (i.e., the entry into the IC). The figure provides some indication that deeper recordings within tonotopic penetrations were less likely to exhibit visual responses. However, this result should be interpreted with caution: due to variance in the length of penetrations and the alignment of the earliest sites, deep locations were somewhat undersampled. A total of 5 out of 30 sites located ≥1 mm along tonotopic penetrations showed visual responsiveness (16.7%). Although small, this proportion significantly exceeds the proportion expected by chance alone of 5% (binomial *p <* 0*.*05). Although not definitive, this suggests that visual responses within tonotopic penetrations are not limited to IC shell regions that the electrode passed through en route to the central nucleus.

Although, visual-related signals were present and statistically significant in all three functionally defined regions, the vigor and time course of the responses differed. **Figure 6** shows PSTHs for the individual responsive sites (black traces) as well as average population PSTHs (red traces) for each of the penetration categories. At untuned sites, visual responses are robust and strongly locked to the onset of the visual stimulus. At tuned sites, the responses are weaker, but some are still quite powerful and clearly locked to the visual stimulus onset. However, the pattern at tonotopic sites is quite different: the PSTHs deviate from baseline (and it is therefore clear why they are categorized as responsive) but

they are weaker, and do not show the kind of crisp coupling to the LED or the saccade evident in at least at some of the sites for the other two categories. In addition, the responses appear to be equally likely to be inhibitory as excitatory.

The tendency of tonotopic sites to show much weaker visualrelated signals is captured differently in **Figure 7**, which shows the distribution of average normalized (units of standard deviation of baseline) firing rate in the epochs used to mark visual responses. Visually sensitive sites in tuned and untuned penetrations generally showed excitatory responses, with untuned penetrations showing larger increases in firing rate. Visual responses in tonotopic penetrations, when found, were much smaller and frequently showed an inhibitory effect. The inset shows the distribution of firing rates for sites not marked as significantly responsive, these distributions were highly similar across categories.

LFPs also showed clear changes following onset of the visual stimulus. **Figures 8A–C** shows normalized LFPs aligned to the onset of the LED for all recorded sites in Monkey A (including both those that showed a significant spiking response as well as those that did not). Interestingly, despite the clear differences seen between spiking responses in each of the functionally defined subregions (**Figures 7**, **8**), LFPs were quite similar. It is unclear whether changes in evoked potentials recorded are truly indicative of dendritic synaptic activity within each of subregions, or if they are merely volume-conducted changes from nearby regions. Clear differences are seen between LFPs following presentation of auditory stimuli in each of these subregions (**Figures 8D–F**), suggesting that this measure can reveal differences locally within the IC. These results support the view that despite the lack of plentiful and powerful overt spiking responses in the tonotopic IC, visual information may potentially reach this subregion.

To summarize, the sample along tonotopic penetrations is limited, and spiking responses at these sites tend to be subtle. This makes it difficult to be sure whether there are indeed signals that can reasonably be referred to as "visual" along these penetrations. The available evidence suggests that, if present, visual signals are sparser, weaker, and less temporally coupled to the onset of the visual stimulus in the tonotopic part (putatively the central nucleus) than in other regions of the IC. Nonetheless, it is clear that visually sensitive cells are not confined to a single region in the tissue surrounding the central nucleus.

#### **SPATIAL SENSITIVITY**

Though a variety of visual receptive field shapes and sizes among IC neurons were identified in our previous study (Porter et al., 2007), population analysis was previously precluded by a data set that was not collected from locations distributed throughout the structure. To summarize spatial receptive field characteristics, we fit two-dimensional Gaussian surfaces to the activity in each of the epochs used for assessing visual responses based on the eye-centered location of the stimulus (see Materials and Methods). We allowed both positive (i.e., peaks) and negative amplitude (i.e., troughs) surfaces. A negative amplitude function was not necessarily indicative of an inhibitory response, the spatial sensitivity was simply better characterized by a circumscribed lower-response region than a circumscribed higherresponse region.

The strongest trends were seen when using a response window 50–250 ms window following stimulus onset (this result is predicted as this window was chosen to capture extrafoveal visual sensitivity). **Figure 9** shows the receptive field locations of all sites that both showed a significant response in this window (*t*-test, *p <* 0*.*05) and were successfully fit by a Gaussian function (*f*-test, *p <* 0*.*05). Of 71 sites meeting the first of these criteria, 29 sites (∼41%) met both. Red points in **Figure 9** indicate the locations of centers of positive amplitude Gaussian fits, and blue points indicate locations of centers of negative amplitude Gaussian fits. Ellipses show the full width half maximum in the horizontal and vertical dimensions, indicating the size and

shape of the receptive field. Positive amplitude Gaussian functions dominate the right hand side of the figure, and negative amplitude functions dominate the left. These results show that, across the population, recordings were more likely to show excitatory visual responses when the stimulus was in the contralateral hemifield. Indeed, points in **Figure 9** that do not fit the trend show very small receptive fields, perhaps indicative of spurious receptive field identification. An alternative analysis using multivariate regressions to characterize spatial tuning produced comparable results: slopes of the horizontal component of regressions were biased in favor of increased activity when the stimulus was in the contralateral hemifield (data not shown). The design of our study did not allow for establishing the frame of reference of these response patterns. However, if visual information were strongly and uniformly head-centered, the eye-centered analysis employed here would have been handicapped at revealing spatial sensitivity.

#### **DISCUSSION**

We presently report that visual responses in the IC are not confined to a single subregion: they are well spread in the structure. In our most peripheral recordings, along penetrations that showed little to no evidence of tuning to sound frequency, the majority of sites were visually sensitive (38/47 sites). In recordings along penetrations that showed tuning to sounds, but no evidence of a tonotopic progression, we found fewer visual-related responses (35/110 sites). Along tonotopic penetrations, those that likely passed through the central nucleus, a connection was seen between visual stimuli and neural response (11/42 sites), but it involved notably weaker responses as seen in **Figures 7**, **8**.

The fact that visually responsive neurons are not confined to a small subregion within the IC has important implications. Somatosensory inputs to the IC in the cat are restricted. Their presence has been identified both anatomically and physiologically in the lateral nucleus of the structure but not elsewhere (Aitkin et al., 1978, 1981). Interestingly, the same lateral region has been highlighted in research in owls as a putative site of audiovisual integration (Feldman and Knudsen, 1997; DeBello et al., 2001). Auditory responses of neurons in the owl lateral nucleus can be modulated by a visual stimulus (Bergan and Knudsen, 2009), and exhibit overt visual responses when inhibitory signals from the optic tectum are blocked pharmacologically (Gutfreund et al., 2002). Coupled with results in the somatosensory domain, this suggests the lateral nucleus may have a specialized role in multisensory integration: computations underlying the combination of the senses might be handled by a discrete set of circuits in a localized part of the IC.

Our findings offer partial support for but also refinement of this view. In primates, the effects of visual stimuli are spread through the IC, but are clearly stronger in the regions showing untuned or tuned but not tonotopic auditory responses. We found only minimal sensitivity to visual stimuli along tonotopic penetrations. This suggests that visual signals in the central nucleus, the principal ascending auditory region of the IC, are, if indeed present, weaker/sparser. The IC is a three dimensional structure, so recording penetrations through the central nucleus likely passed through shell regions as well. Indeed, it is possible that the small visual signals we did observe along tonotopic penetrations may have come from the shell locations before or after the tonotopic domain. Additional sampling with finer spacing along tonotopic penetrations is necessary to confirm that visual responses are found at above chance levels within this region. Thus, compelling proof of whether or not neurons in the central nucleus show modulation based on visual signals is still needed. In any event, it is now apparent that the strength and prevalence of visual influences is certainly reduced in the tonotopic area relative to surrounding tissue. Subthreshold evoked potentials, however, were fairly similar across subregions of the IC. These results indicate the potential for visual *modulation* of auditory activity in all regions of the IC when stimuli are presented concurrently. Indeed, auditory cortex has shown LFP changes to non-auditory stimuli in the absence of changes in firing rate (Lakatos et al., 2007). This input can modulate bimodal spiking activity without having a unimodal effect, a pattern also seen in the owl IC (Bergan and Knudsen, 2009).

Anatomical studies have revealed multiple visually sensitive regions that project to shell regions of the IC, including connections from visual and parietal cortices (Cooper and Young, 1976; Coleman and Clerici, 1987; Druga et al., 1997), the retina (Itaya and Van Hoesen, 1982; Yamauchi and Yamadori, 1982; Herbin et al., 1994), and the SC (Adams, 1980; Coleman and Clerici, 1987; Covey et al., 1987). Direct inputs to the central nucleus from visual areas have not been conclusively demonstrated, though such information could potentially reach the IC through less direct routes. The weak visual modulation we observed along

see Materials and Methods). For display, traces were smoothed by convolving the raw data with a 10 ms Gaussian kernel normalized to sum to 1. The red traces indicate the mean of the responses, means were taken from the unsmoothed data.

tonotopic penetrations might have arisen through such connections. Future studies with concentrated sampling in the central nucleus will be able to conclusively indicate whether centrally located neurons are sensitive visual input.

Visual information may also reach the IC through descending connections within the auditory pathway. A number of papers have reported both modulation and overt effects of visual stimuli in the core and belt regions of auditory cortex [humans: (Sams et al., 1991; Calvert et al., 1999; Giard and Peronnet, 1999; Callan et al., 2003; Wright et al., 2003; Besle et al., 2004, 2008; Molholm et al., 2004; van Wassenhove et al., 2005; Martuzzi et al., 2007; Luo et al., 2010); monkeys: (Barraclough et al., 2005; Brosch et al., 2005; Ghazanfar et al., 2005; Kayser et al., 2007, 2008, 2010); ferrets: (Bizley et al., 2007; Bizley and King, 2008); reviews: (Calvert, 2001; Ghazanfar and Schroeder, 2006; Kayser et al., 2009); appendix]. Descending connections from the auditory cortex more heavily target the shell regions of the IC than the central nucleus (Diamond et al., 1969; Andersen et al., 1980; Druga and Syka, 1984a,b; Coleman and Clerici, 1987; Saldaña et al., 1996; Druga et al., 1997; Winer et al., 2002; Schofield, 2009), consistent with the response trend we observed.

We also conducted the first population analysis of spatial sensitivity to visual stimuli in the IC. We found that spatial tuning was powerfully lateralized with the largest increases in firing rate coming from stimuli in the contralateral hemifield. Auditory responses show similar spatial sensitivity: IC neurons show monotonoic sensitivity with increasing response amplitude as stimuli are presented farther contralaterally (Groh et al., 2003). Further study on single unit responses will be necessary to determine whether individual cells show a relationship between visual and auditory tuning, but evidence from the population suggests that there may be a link.

The contralateral pattern of visual receptive fields parallels that observed for eye position, where firing rates increase as the eyes are oriented more contralaterally (Bulkin and Groh, 2012) However, the two effects are distinct, and neither can fully account for the other. For example, if the IC were not actually sensitive to visual stimulus location but only to eye position, this would appear in our study as a preference for visual stimulus locations in the opposite hemifield. When the eyes are directed more contralaterally, evoking higher eye-position related activity, the location of the fixation LED would lie more ipsilaterally on the retina, and neurons would appear to have ipsilateral visual receptive fields. We did not observe this pattern, suggesting that eye position and visual signals influence IC neurons independently.

These results raise questions about how auditory, visual, and eye-position signals interact in the IC. To combine the senses, the brain must reconcile signals that arise in different frames of reference: auditory localization is anchored to the head, and visual localization is anchored to the (independently moveable) eyes. In our assessment of spatial sensitivity we used an eye-centered definition of visual location, but the coordinate system used by the brain may be different. On average, auditory information in the IC is not encoded in head-centered frame of reference (as might be assumed given the head centered methods for assessing sound location). Rather auditory response patterns are modulated by the position of the eyes, though not enough to transform them fully into eye-centered co-ordinates (Groh et al., 2001; Zwiers et al., 2004; Porter et al., 2007; Maier and Groh, 2010). Visual signals may show a similar pattern, with receptive fields that are not fully described in a retinotopic reference frame. Such results have been observed in lateral and medial intraparietal cortex, representations of visual and acoustic information span a continuum from head- to eye-centered (Mullette-Gillman et al., 2005, 2009).

Along tonotopically organized penetrations through the IC, putatively the central nucleus, overt visual effects are weak at best while eye position modulation is still quite strong (Bulkin and Groh, 2012). While responses to visual stimuli are weak in the central nucleus, the effect of visual cues during bimodal stimulus presentation remains untested. Visual input may reach the central nucleus, but be ineffective in changing spike rate in the absence of auditory stimuli, a pattern similar observed with somatosensory effects in auditory cortex (Lakatos et al., 2007). Future work using presentations of bimodal stimuli will investigate this possibility.

## **REFERENCES**

in auditory cortex. *Cereb. Cortex* 17, 2172–2189.

Neural processes underlying perceptual enhancement by visual speech gestures. *Neuroreport* 14, 2213–2218.

multisensory? *Trends Cogn. Sci.* 10, 278–285.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 05 May 2012; accepted: 18 August 2012; published online: 05 September 2012.*

*Citation: Bulkin DA and Groh JM (2012) Distribution of visual and saccade related information in the monkey inferior colliculus. Front. Neural Circuits 6:61. doi: 10.3389/fncir.2012.00061*

*Copyright © 2012 Bulkin and Groh. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.*

## Sounds and beyond: multisensory and other non-auditory signals in the inferior colliculus

#### *Kurtis G. Gruters 1 \* and Jennifer M. Groh1,2,3,4*

*1 Department of Psychology and Neuroscience, Duke University, Durham, NC, USA*

*2 Department of Neurobiology, Duke University, Durham, NC, USA*

*3 Duke Institute for Brain Sciences, Duke University, Durham, NC, USA*

*4 Center for Cognitive Neuroscience, Duke University, Durham, NC, USA*

#### *Edited by:*

*Manuel S. Malmierca, University of Salamanca, Spain*

#### *Reviewed by:*

*Sarah L. Pallas, Georgia State University, USA Susan Shore, University of Michigan, USA*

#### *\*Correspondence:*

*Kurtis G. Gruters, Department of Psychology and Neuroscience, Duke University, 308 Research Drive, Box 90999, Durham, NC 27708, USA. e-mail<EMAIL_ADDRESS>
The inferior colliculus (IC) is a major processing center situated mid-way along both the ascending and descending auditory pathways of the brain stem. Although it is fundamentally an auditory area, the IC also receives anatomical input from non-auditory sources. Neurophysiological studies corroborate that non-auditory stimuli can modulate auditory processing in the IC and even elicit responses independent of coincident auditory stimulation. In this article, we review anatomical and physiological evidence for multisensory and other non-auditory processing in the IC. Specifically, the contributions of signals related to vision, eye movements and position, somatosensation, and behavioral context to neural activity in the IC will be described. These signals are potentially important for localizing sound sources, attending to salient stimuli, distinguishing environmental from self-generated sounds, and perceiving and generating communication sounds. They suggest that the IC should be thought of as a node in a highly interconnected sensory, motor, and cognitive network dedicated to synthesizing a higher-order auditory percept rather than simply reporting patterns of air pressure detected by the cochlea. We highlight some of the potential pitfalls that can arise from experimental manipulations that may disrupt the normal function of this network, such as the use of anesthesia or the severing of connections from cortical structures that project to the IC. Finally, we note that the presence of these signals in the IC has implications for our understanding not just of the IC but also of the multitude of other regions within and beyond the auditory system that are dependent on signals that pass through the IC. Whatever the IC "hears" would seem to be passed both "upward" to thalamus and thence to auditory cortex and beyond, as well as "downward" via centrifugal connections to earlier areas of the auditory pathway such as the cochlear nucleus.

**Keywords: multisensory, inferior colliculus, auditory, sound localization, communication**

## **INTRODUCTION**

Organisms gather information about their environment from a variety of sensory systems, but how these sensory systems interact with each other is poorly understood. Multisensory integration is sufficiently common in cortical regions that the cortex has been described as a fundamentally multisensory processor [for review, see Ghazanfar and Schroeder (2006)]. In contrast, relatively little attention has been given to the subcortical systems that provide sensory information to the cortex. Much of the sensory information passed along to the cortex from subcortical areas is already multisensory in nature, and is influenced by behavioral state and relevance of stimuli. In this review, we focus on the multisensory and context-related connections and response properties of the inferior colliculus (IC), an important subcortical node in the auditory pathway.

The IC is particularly interesting in regard to multisensory and other non-auditory contributions to hearing as it is a necessary relay for nearly all ascending and descending auditory information [for review see Winer and Schreiner (2005)]. Situated relatively early in the auditory system, the IC is comprised of a central nucleus (ICC; see **Table 1** for list of abbreviations) and various surrounding shell nuclei (shell nuclei of the IC1 ; sIC collectively), including, but not limited to, the external IC (ICX), pericentral nucleus of the IC (ICP), dorsal and lateral cortices of the IC, and brachium of the IC. The ICC primarily sends ascending auditory information to the thalamus (e.g., Kudo and Niimi, 1980; Calford and Aitkin, 1983) which then proceeds toward the cortex. The sIC also send ascending projections to the thalamus (ICP and ICX projections: Kudo and Niimi, 1980; and

1Though the subnuclear anatomy of the IC has been well described elsewhere (e.g., Morest and Oliver, 1984), the names and precise borders of these nuclei are not used consistently in the studies discussed in this review. Therefore, this core-shell nomenclature is adopted to avoid potentially applying these more precise subnuclear definitions inappropriately, particularly when such information is missing or ambiguous. When information about subnuclear locations is available for a given study, we have attempted to include the terminology used by the authors in our description of their findings.

#### **Table 1 | List of anatomical abbreviations.**

Calford and Aitkin, 1983) as well as the superior colliculus (SC) (brachium of the IC, ICX: Van Buskirk, 1983; dorsomedial part of the IC, ICX: Druga and Syka, 1984; ICX: Zhang et al., 1987; rostal pole of the IC: Harting and Van Lieshout, 2000), and descending information back to the auditory brainstem (including the ICC) (Huffman and Henson, 1990).

Converging anatomical and physiological evidence indicates that cells within the IC are sensitive to visual, oculomotor, eye position, and somatosensory information as well as to signals relating to behavioral context and reward (**Figure 1**). Auditory perception and behavior are likely to be shaped by these nonauditory inputs to the IC.

## **NON-AUDITORY INFLUENCES ON NEURAL ACTIVITY IN IC VISION AND OCULOMOTOR INFLUENCES**

Numerous anatomical studies have established the existence of direct retinal innervation of the contralateral IC (rat, monkey: Itaya and Van Hoesen, 1982; rat: Yamauchi and Yamadori, 1982; guinea pig, hamster, rat: Zhang, 1984; mole-lemming: Herbin et al., 1994). Projections from the retina pass through the contralateral SC and into the ICP near the midline, fanning out dorsolaterally, before being pruned back during early neural development (Cooper and Cowey, 1990). Enucleation of the contralateral eye causes the degeneration of these connections in adult animals (Paloff et al., 1985). In addition to receiving retinal efferents, both the ICC and sIC (in particular, the ICX) receive inputs from the visual cortex (primarily ipsilateral) (cat: Cooper and Young, 1976) and the ipsilateral SC, a visually responsive structure involved in programming saccadic eye movements (cat: Adams, 1980; bat: Covey et al., 1987; rat: Coleman and Clerici, 1987; barn owl: Hyde and Knudsen, 2000) [for review of saccade generation in the SC, see Gandhi and Katnani (2011)].

The presence of anatomical connections from visual processing sources suggests that IC neurons should be responsive to visual stimuli. Several physiological studies have investigated this hypothesis and established that there are cells in the IC whose auditory responses are modulated by a concurrent visual stimulus (Syka and Radil-Weiss, 1973; Tawil et al., 1983), or that are capable of responding directly to visual stimuli without an accompanying sound (Mascetti and Strozzi, 1988; Porter et al., 2007; Bulkin and Groh, 2012b). Early reports using anesthetized and paralyzed cats suggested that 8–9% of IC neurons were visually responsive (Tawil et al., 1983: 112 total cells tested; Mascetti and Strozzi, 1988: 91 total cells tested), but later work involving more extensive statistical testing as well as awake and behaving monkeys performing a visually guided saccade task suggests that the proportion is much higher (Porter et al., 2007; Bulkin and Groh, 2012b). Porter et al. (2007) found a variety of response profiles including excitation (35%) or inhibition (5.5%) in response to a visual stimulus, excitation in conjunction with a visual stimulus and its accompanying saccade (15%), or excitation just during the saccade (4.5%). An additional 6% exhibited delayed activity increases or activity that differed in a non-specific but statistically significant fashion from baseline during stimulus presentation and/or the saccade. Overall, 64% of the tested neuronal population (*n* = 180) displayed statistically significant responses to visual stimuli and/or saccade-related activity.

A more detailed mapping of the locations of visual- and saccade-related responses in the IC has revealed that visual response properties are not uniform throughout the IC. Bulkin and Groh (2012b) localized visually responsive neurons with respect to a previous systematic mapping of auditory function in the region (Bulkin and Groh, 2011) (**Figure 2**). To define this functional map, Bulkin and Groh (2011) used a combination of electrophysiological recording, stereotaxic coordinates, MRI, histology, and known physiological properties of IC subdivisions to identify and demarcate three regions with distinct response properties. A central tonotopic region (red region in **Figures 2A,B**; example penetration given in **Figure 2E**), likely situated well within the ICC, exhibited cells whose best frequency increased as the electrode advanced along a dorsolateral to ventromedial trajectory. The surrounding region contained neurons tuned for low-frequency sounds but lacked a tonotopic gradient (low-frequency tuned area; purportedly overlapping with the outer portions of the ICC as well as the sIC) (green region of **Figures 2A,B**; example penetration in **Figure 2D**). Cells within the final region at the periphery of the IC were either unresponsive to pure tones or non-selective for tone frequency (blue region of **Figures 2A,B**; example penetration in **Figure 2C**).

Visual- and visuomotor-responses were found distributed throughout the IC (Bulkin and Groh, 2012b). However the different regions exhibited different proportions of sites sensitive to visual stimuli as well as different patterns of responses at those sites. Untuned auditory areas showed robust visual- or saccaderelated responses in 81% of the tested units. Low-frequency tuned sites also showed vigorous visual- and saccade-related firing, but the responses were less strong than untuned sites and were found in only 31% of these cells. Tonotopic penetrations yielded

the lowest proportion of responsive cells (26%). Visual-related responses observed on these penetrations were weak, deviating only slightly from baseline, and did not exhibit the same clear stimulus- or saccade-onset timing present in untuned and low-frequency tuned populations.

The relative contributions of each of the potential anatomical sources of visual input to the IC are not known. Response latency is only moderately informative: signals within the IC cannot occur faster than their source signal, but reported latencies span a range consistent with any of the potential sources. Tawil et al. (1983) reported latencies ranging between 20 and 30 ms for the nine visually responsive cells recorded from within the cat IC, consistent with direct retinal innervation (latency in cat optic tract ≈20 ms: Freund et al., 1972), but excluding presumably longer latency inputs from the SC or visual cortex. However, in monkeys, latencies were reported to range from 60 to 115 ms (Porter et al., 2007), compatible with visual latencies from visual cortex, SC, and even the retina under low intensity stimulation (latency in monkey visual cortex ≈55–130 ms: Schmolesky et al., 1998; monkey SC ≈ 60–100 ms: Bell et al., 2006; monkey optic tract ≥40 ms: Inoue et al., 2000).

Likewise, the properties of the visual receptive fields (vRFs) found in the IC do not obviously implicate any one of the known inputs to the exclusion of the others. Like all three potential input sources, the IC's representation favors the contralateral field, but unlike the possible inputs, no topographic organization within the contralateral field has been observed in mammals. The vRFs of individual cells are variable in size, ranging from tens of degrees to nearly half of the visual field (Mascetti and Strozzi, 1988; Porter et al., 2007; Bulkin and Groh, 2012b).

This is larger than the typical RFs found in retinal ganglion cells, visual cortex, or the SC (striate cortex: Wurtz, 1969; SC: Goldberg and Wurtz, 1972; retinal ganglion cells: Hammond, 1974; and Cleland et al., 1979).

The types of stimulus selectivity seen in IC neurons are also evident in more than one of the potential input sources. For example, visually responsive neurons in the IC tend to respond well to both static (Porter et al., 2007; Bulkin and Groh, 2012b) and moving (Mascetti and Strozzi, 1988) stimuli, properties observed in both the visual cortex (Wurtz, 1969) and retina (Shapley and Perry, 1986). IC cells that respond to moving stimuli are not tuned to a particular direction of stimulus motion (Mascetti and Strozzi, 1988), which is similar to a subset of SC neurons that are directionally untuned (Humphrey, 1968; Rhoades and Chalupa, 1976). Thus, the retina, visual cortex, and the SC all share some aspects of stimulus selectivity with IC neurons. Additional research is therefore necessary to explore receptive fields and stimulus selectivity in more detail before reaching conclusions on the potential source, or synthesis of multiple sources, responsible for IC vRFs and response properties.

Visual sensitivity has also been reported in the barn owl IC, but in contrast to mammalian studies, visual spatial sensitivity in the barn owl ICX appears to be well organized and tuned to the auditory space map in the same region (Bergan and Knudsen, 2009). Visual modulation fields [vMFs; defined as the region of visual space in which a stimulus exhibits ≥50% of its maximal modulatory effects on the paired auditory stimulus (see **Figure 3**)] of neurons throughout the ICX are well correlated with the location predicted by auditory localization cues. That is, a visual stimulus occurring at a given location in the visual field can modulate the ICX response to sounds originating from a similar point in space (**Figure 3**). Also in contrast to mammals, neurons in the owl ICX do not appear to respond to visual stimuli alone (Gutfreund et al., 2002; Bergan and Knudsen, 2009). Instead, visual information projecting from the owl's optic tectum (OT; homologous to the mammalian SC) to the ICX is gated via GABAergic inhibition in the OT (Gutfreund et al., 2002). Multiunit activity in the ICX is sensitive to visual stimulation only if inhibition is blocked in the region of OT corresponding to those units' preferred auditory location.

A variety of potential factors might influence the proportions and response patterns of visually sensitive cells in the IC. First, different species may utilize visual information in different ways depending on their ecological niche. For instance, diurnal species such as monkeys (e.g., Porter et al., 2007; Bulkin and Groh, 2012b) may exhibit response patterns that differ from those of nocturnal species such as cats or barn owls (e.g., Tawil et al., 1983; Mascetti and Strozzi, 1988; Bergan and Knudsen, 2009). Second, the cognitive state of the animal may be an important factor: Porter et al. (2007), and Bulkin and Groh (2012b) both used awake and behaving animals and found the highest proportion of visually responsive cells in comparison to other studies. Bergan and Knudsen (2009) estimated a slightly lower proportion in their awake but restrained birds 2 , while the anesthetized preparations used by Tawil et al. (1983) and Mascetti and Strozzi (1988) yielded the smallest proportions. As discussed in detail in the section on "Behavioral context" of this review, behavioral state has a profound effect on neural activity in the IC. The report by Gutfreund et al. (2002) suggests that behavioral state might exert

2They estimated that approximately one-third of their sampled neurons were sensitive to audiovisual stimuli; however, the aim of their study was to describe the response patterns of these neurons, so they did not report an exact proportion.

a specific influence over visual responsiveness in the IC by gating the passage of visual information into the IC. Importantly, the SC (or OT) is commonly implicated in both oculomotor (Gandhi and Sparks, 2007) and attentional control (e.g., Shen et al., 2011), and serves as one of the routes by which visual input can reach the IC. The use of a saccade task might therefore have been an important factor increasing the proportion of visually responsive cells in both Porter et al. (2007) and Bulkin and Groh (2012b). Other influential factors may include the breadth of sampling of visual space and the type of visual stimuli used. Visually responsive neurons in other brain areas are known to respond only to stimuli presented within spatially restricted receptive fields and to stimuli of preferred orientations, colors, shapes, and so forth (Schiller, 1986). It is likely that the set of visual stimuli used in the aforementioned studies did not exhaust all of the parameters for which visually responsive cells in the IC are selective. Therefore, the reported proportions may well be an underestimate of the actual proportion of visually responsive cells.

visual modulation field (red field). Adapted from Gutfreund et al. (2002); and

Bergan and Knudsen (2009).

The functional purpose of visual signals in the IC is unknown. One potential role is to help calibrate the representation of auditory space. This hypothesis has been tested thoroughly in the owl [for a review, see Knudsen (2002)], where visual space maps in the OT are functionally and anatomically connected to the auditory space maps of the ICX (Bergan and Knudsen, 2009). Barn owls reared with prisms that displace visual space show altered auditory spatial sensitivity in the ICX (Brainard and Knudsen, 1993), altered connectivity between the ICC and ICX (Debello et al., 2001), and altered connectivity patterns from the ICX to OT (Hyde and Knudsen, 2002; Linkenhoker and Knudsen, 2002). Ocular enucleation in rats appears to alter auditory spatial sensitivity as well (Pageau et al., 2008). This may be due to visual deprivation, though impaired eye position signals are also likely to result from enucleation and should not be dismissed as a contributing factor (see the section on "Eye position"). Together, these studies indicate that coding of auditory location is at least partially dependent on visual input. However, calibration of auditory space may not be the only role of visual signals in the IC. A possible role of visual signals in the sIC for communication is discussed in the section on "Communication."

### **EYE POSITION**

The eyes and ears necessarily receive visual and auditory spatial information, respectively, within a different frame of reference. Specifically, visual space is initially encoded based on where the image falls on the retina (a so-called eye-centered reference frame) while auditory space is calculated based on the position of the sound relative to the ears and the head (a head-centered reference frame). In species where the eyes are able to move to a substantial degree within the head (e.g., rhesus monkeys and cats but not rodents or barn owls), the visual and auditory reference frames are not fixed to each other (**Figure 4**). Because of this constantly changing relationship between reference frames, aligning visual and auditory space requires factoring in both the orbital position of the eyes and sound localization cues.

Groh et al. (2001) identified a subset of cells in the IC (approximately one-third: 24/73) that altered their firing patterns in response to an auditory stimulus depending on where the eyes were positioned in their orbits with respect to the head. More specifically, the firing rate of individual neurons usually tended to increase as a monotonic function of eye position, with the population favoring contralateral eye positions (relative to IC hemisphere) (Porter et al., 2006) (**Figure 5A**). Additionally, eye position sensitivity appeared to be more common among neurons insensitive to sound location (up to 32% for sound location sensitive units and 56% for insensitive units; Porter et al., 2006), suggesting somewhat segregated subpopulations for eye position and sound location sensitive units in the IC. Eye position sensitivity has also been found for vertical eye positions (Zwiers et al., 2004; Bulkin and Groh, 2012a), as well as during both task-related and spontaneous fixations, and with or without the presence of a concurrent sound stimulus (Porter et al., 2006; Bulkin and Groh, 2012a). Collectively, these results indicate that eye position signals are an important aspect of IC processing and are separable from signals related to head-centered sound location, vision, and eye movements.

The reference frame in the IC has been found to be neither head-centered nor eye-centered but somewhere in between. Porter et al. (2006) reported that only 16% of tested units were head-centered, whereas 9% were more eye-centered; the remaining 75% of the tested units were approximately equally consistent with either head- or eye-centered reference frames (**Figure 5B**). A model trained on these data was able to provide an output that closely approximated a particular sound location via either head- or eye-centered coordinates (Porter et al., 2006). The brain seems to maintain this hybrid encoding scheme until the time of saccade generation. Specifically, the hybrid scheme is similar in the IC, auditory cortex (Werner-Reiss et al., 2003; Porter et al., 2006), and the visual intraparietal sulcus (Mullette-Gillman et al., 2005, 2009) as well as in the sensory signals in the SC (Jay and Sparks, 1984; Populin et al., 2004; Lee and Groh, 2012). However, a motor output command is eventually developed in eye-centered coordinates in the SC at the time of saccade generation (Lee and Groh, 2012).

The location of eye position sensitive cells within the IC was recently mapped in detail (Bulkin and Groh, 2012a) with respect to the functional response map previously described (Bulkin and Groh, 2011; see the section on "Vision and oculomotor influences," and **Figure 2**). Eye position sensitivity was found throughout the IC, but the proportions varied according to the functional response patterns of the recording site. Eye position effects were detected in 25% of tonotopic sites, 33% of low-frequency tuned sites, and 42% of untuned sites. As noted previously, these responses are likely to coincide with the centralmost part of the ICC, the edges of the ICC and the inner part of the sIC, and the outer sIC respectively (Bulkin and Groh, 2011).

Despite the physiological studies identifying eye position signals in the IC, the anatomical sources of these signals have yet to be identified. It is not clear whether such signals are a result of corollary discharge from oculomotor outputs [e.g., the SC, or the fastigial nucleus of the cerebellum (Carpenter, 1959; Earle and Matzke, 1974)], proprioceptive feedback from muscles controlling eye position [along, for instance, the opthalamic tract of the trigeminal nerve (TN) (Steinbach, 1987); cuneate nuclei (Porter, 1986); or from the somatosensory cortex (Zhang et al., 2008)], or some combination of both. These signals may also come from other auditory areas. For instance, the primary auditory cortex contains eye position signals (Werner-Reiss et al., 2003) and sends anatomical projections to the IC (Winer et al., 1998). It is possible that eye position signals are sent through this route via corollary discharge from cortical regions important for eye position and movements (after Sommer and Wurtz, 2008). Further investigation will be necessary to identify the connectivity of eye position sensitive neurons in the IC.

#### **SOMATOSENSATION**

The IC also receives input from the somatosensory system. Connections from various brainstem nuclei—including the trigeminal nerve ganglion (TNG) and nuclei of the dorsal column (nDC)—have been identified in both the intercollicular region (cat: Anderson and Berry, 1959; opossum: Robards et al., 1976), and the ICX (opossum: Robards, 1979; cat: Aitkin et al., 1981; monkey: Wiberg et al., 1987; rat: Coleman and Clerici, 1987; hedgehog: Kunzle, 1998). Additionally, both the motor and somatosensory cortices project to the ipsilateral ICC and ICX

(Cooper and Young, 1976). The ICC may also receive somatosensory signals from other areas along the auditory pathway. For example, the ICC is heavily innervated by the dorsal cochlear nucleus (DCN) (e.g., Cant and Benson, 2008), which is also known to receive direct innervation from the nDC (Li and Mizuno, 1997) and the TN (Zhou et al., 2007), and whose principle output cells are modulated by TN stimulation (Koehler et al., 2011). The ICX, meanwhile, receives convergent inputs from the DCN and spinal trigeminal nucleus (Zhou and Shore, 2006), suggesting that multiple, overlapping circuits may be involved in creating somatosensory-sensitive cells in the IC.