Quantitative data were obtained from 452 VIP neurons recorded from 7 hemispheres in 5 rhesus monkeys. The majority of neurons were recorded from the right and left hemispheres of animal J and the right hemisphere of animal C, as illustrated in (black symbols: cells with significant responses to visual and/or vestibular translation; white symbols: cells without significant responses to translation). Cells responsive to translation were encountered in both the upper and lower banks of the intraparietal sulcus, with recordings concentrated around the medial tip of the sulcus. Upon isolation, each VIP neuron was first tested with physical (vestibular condition) and simulated (visual condition) translation along 26 motion directions uniformly distributed in 3D space (). If satisfactory isolation was maintained throughout this translation protocol, some cells (see Materials and Methods) were subsequently tested with physical and simulated rotation about the same 26 axes, with each axis defining a direction of rotation according to the right-hand rule. Each movement followed a Gaussian velocity profile, with a corresponding biphasic acceleration profile (). For each block of trials (translation or rotation), visual and vestibular conditions were randomly interleaved, along with a null condition in which monkeys fixated the head-centered target without any visual or vestibular stimulation. We begin by describing the properties of VIP responses to translation.
Visual and vestibular responses to translation
Typical responses from a ‘congruent’ cell and an ‘opposite’ cell are illustrated in , respectively. Both of these neurons have single-peaked tuning, with a single significant epoch of directional selectivity. The plots on the left show average PSTHs for all 26 directions of vestibular (top) and visual (bottom) translation, arranged according to stimulus direction in spherical coordinates. Red dashed lines show the peak response times for each neuron under each stimulus condition. A peak time is defined as the center of the 400 ms time window that produces the largest departure in firing rate from the baseline response (see Materials and Methods).
The 3D directional tuning of these example neurons, computed at the corresponding peak times, is shown as color contour maps (elevation versus azimuth) on the right side of . The preferred direction of each neuron was defined as the azimuth and elevation of the vector sum of the neural responses (see Materials and Methods). The congruent cell of has similar direction preferences for vestibular and visual translation stimuli; [azimuth, elevation] = [−24°, −38°] and [−5°, −64°], respectively, corresponding to an upward and slightly rightward trajectory. In contrast, the opposite cell in preferred a backward/rightward direction in the vestibular condition ([azimuth, elevation] = [−64°, 7°]) and a forward/leftward direction in the visual condition ([azimuth, elevation] = [129°, 7°]). As described further below, congruent and opposite cells were both frequently encountered in VIP.
illustrates another example VIP cell, which was classified as ‘double-peaked’ based on its vestibular translation tuning. As illustrated by the vestibular PSTHs (), there were two peak times for this cell, one at 0.91 s (red line) and another at 1.41 s (green line). The cell showed significant directional tuning at both peak times, with nearly opposite direction preferences at [azimuth, elevation] = [39°,−16°] and [−126°, 9°], respectively (). This occurs because the cell’s spatio-temporal response profile has two distinct temporal epochs of direction tuning. The early peak time occurs just prior to peak stimulus velocity, whereas the second peak time occurs after the second peak of the stimulus acceleration profile. Note that the visual response of this same neuron was single-peaked ().
Figure 3 Example of a VIP neuron with double-peaked direction tuning in the vestibular condition. (A), (B) PSTHs for 26 directions during presentation of vestibular (A) and visual (B) translation stimuli. The red and green lines indicate the two peak times for (more ...)
Among 452 cells tested in VIP, 222 (49%) showed significant tuning for vestibular translation, as compared to 50–60% in MSTd (Gu et al., 2006
; Takahashi et al., 2007
; Liu and Angelaki, 2009
). Unlike in MSTd, where >95% of the cells show significant tuning for visual translation simulated by optic flow (Gu et al., 2006
), only 310/452 (69%) of VIP cells showed significant directional tuning for visual translation. Of these, 182 cells (40%) were significantly tuned (p<0.01, ANOVA) to both visual and vestibular translation, 128 cells (28%) were tuned to visual stimuli only, and 40 cells (9%) were tuned to vestibular stimuli only (such cells with only vestibular tuning are extremely rare in MSTd). All of these proportions were significantly greater than expected by chance (p<0.001, chi-square test). As shown in , only 3% of visual responses to translation in VIP were classified as double-peaked, whereas ~21% of the vestibular translation responses were double-peaked. Thus, single-peaked responses predominated.
Classification of tuned cells as ‘single-peaked’ and ‘double-peaked’
Cells with significant directional tuning were further subdivided based on whether the spatial tuning (at a particular peak time) was unimodal or multimodal (see Materials and Methods, Chen et al., 2010
). The vast majority of VIP neurons showed unimodal direction tuning: 83% (184/222) for vestibular translation and 77% (240/310) for visual translation. Direction preferences of VIP neurons with significant unimodal tuning were distributed throughout the spherical stimulus space, as illustrated in (vestibular translation) and (visual translation). Each data point in these scatter plots specifies the preferred 3D direction of a single neuron (black: multisensory cells; red: vestibular-only; green: visual-only), while histograms along the boundaries show the marginal distributions of azimuth and elevation preferences. As in MSTd (Gu et al., 2006
; Gu et al., 2010
), the distribution of azimuth preferences for VIP visual responses was significantly bimodal (p
0.001, uniformity test; puni
< 0.001, pbi
= 0.97, modality test; see Materials and Methods), with most cells preferring lateral over forward/backward directions. Among 240 cells with significant visual translation tuning, none had direction preferences within ±30° of the backward axis and only 11 cells (4.6%) had preferred directions within ±30° of straight forward (dashed oval regions in ).
Figure 4 Summary of direction tuning properties of VIP neurons during translation. (A), (B) Distribution of vestibular (A) and visual (B) 3D heading preferences. Each data point in the scatter plot corresponds to the preferred azimuth (abscissa) and elevation (more ...)
A similar tendency toward a bimodal distribution of azimuth preferences was seen for vestibular translation but the effect was not significant. The distribution of azimuth preferences was not significantly different from uniform (p=0.35, uniformity test), although slightly more cells preferred lateral and vertical movements. Only 4/184 (2.2%) cells with significant vestibular translation tuning had a preferred direction within ±30° of straight backward and 9 cells (4.9%) had a preferred direction within ±30° of straight forward (dashed oval regions in ). The distributions of elevation preference are significantly non-uniform, but unimodal, for both visual and vestibular responses (p < 0.01, uniformity test; puni > 0.28, modality test).
As illustrated in , which show the tuning width at half maximum (in the horizontal plane) from a spline fit (interpolated at the 1° resolution) versus azimuth preference for each neuron, tuning was generally broad independently of azimuth preference, and tuning width was similar for unisensory and multisensory cells. In comparison to area MSTd (Gu et al., 2006
; Gu et al., 2010
), we found that the average tuning width in VIP was significantly narrower for the visual translation condition (p=0.001, Wilcoxon rank-sum test). However, there was no significant difference in tuning width between areas for the vestibular translation condition (p=0.17).
As shown by the examples of , some VIP cells have similar direction preferences for visual and vestibular stimuli, whereas others have opposite preferences. The distribution of the absolute difference in direction preference (|Δ Preferred direction|) between visual and vestibular stimulus conditions was strongly bimodal (p = 0.007, uniformity test; puni
= 0.53, modality test), as illustrated in . As found previously for translation responses in MSTd (Gu et al., 2006
), congruent and opposite neurons were encountered in roughly equal proportions in VIP: 37% (44/118) had |Δ Preferred direction| < 60°, and 42% (50/118) had |Δ Preferred direction| > 120°. In subsequent figures, for simplicity, we refer to cells with |Δ Preferred direction| < 90° as ‘congruent’ and those with |Δ Preferred direction| > 90° as ‘opposite’.
Figure 5 Comparison of tuning preferences and direction selectivity between visual and vestibular responses of VIP neurons during translation. (A) Distribution of the absolute difference in 3D preferred direction (|Δ Preferred direction|) between visual (more ...)
The strength of direction tuning of VIP neurons was quantified using a direction discrimination index (DDI), which ranges from 0 (poor tuning) to 1 (strong tuning). DDI values for visual and vestibular translation conditions are compared in . Considering all cells (n=452), the vestibular DDI was significantly smaller than the visual DDI (Wilcoxon matched pairs test, p<0.001). In addition, multisensory neurons were more strongly tuned, on average, than unisensory neurons. Specifically, the vestibular DDI of multisensory neurons (0.68±0.01, SE) was significantly greater than the vestibular DDI for vestibular-only neurons (0.64±0.01) (p=0.01, Wilcoxon rank-sum test; , black vs. red symbols). Similarly, the visual DDI of multisensory neurons (0.72±0.01, SE) was significantly greater than the visual DDI for visual-only neurons (0.68±0.01) (p<0.001, Wilcoxon rank-sum test; , black vs. green symbols).
Because the experimental protocols and cell sampling criteria used here were identical to those previously used to characterize optic flow and vestibular tuning in MSTd and VPS, a direct comparison between areas is possible. These comparisons are summarized graphically () as cumulative distributions of DDI for VIP (orange), MSTd (black) and VPS (red), shown separately for visual and vestibular responses, respectively. Overall, the vestibular translation DDI for VIP (mean: 0.61 ±0.01, SE) was modestly but significantly greater than that for MSTd (0.59 ±0.01) (p=0.01, Wilcoxon rank-sumtest) but weaker than that for VPS (0.69±0.01) (p<0.01). In contrast, the visual translation DDI for VIP (0.66 ±0.01) was substantially less than that for MSTd (0.76 ±0.01) (p<0.001, Wilcoxon rank-sum test) but greater than that for VPS (0.60±0.01) (p<0.001). Similar results were obtained when tuning strength was quantified as the vector sum of responses across directions or as the raw difference in firing rate between stimuli that elicited maximal and minimal responses (data not shown). Thus, whereas MSTd neurons are more selective to optic flow and VPS neurons are more selective to vestibular inputs, heading tuning in VIP is relatively balanced across the two modalities.
Visual and vestibular responses to rotation
We now turn to the subset of VIP cells (216/452) that was also tested with rotation stimuli. Note that this subset is relatively small because we did not attempt the rotation protocol on some neurons, but rather ran protocols for other studies (see Methods). Among 142 cells for which isolation was maintained and the rotation protocol was completed, 63 (44%) were significantly tuned (p<0.01, ANOVA) to vestibular rotation and 60 (42%) were significantly tuned to visual rotation. About a quarter (36 cells) were multisensory neurons, whereas 24 cells (17%) were tuned to visual stimuli only and 27 cells (19%) were tuned to vestibular stimuli only. The distributions of direction preferences for rotation resembled those for translation overall ( vs. ). The distribution of visual azimuth preferences was significantly bimodal (p < 0.001, uniformity test; puni = 0.02, pbi = 0.97, modality test), with a tendency for neurons to prefer pitch rather than roll rotations (). Because of the limited size of the data set, none of the other distributions of azimuth or elevation preferences were significantly different from uniform (p>0.05).
Figure 6 Summary of tuning properties of VIP neurons during rotation. (A), (B) Distribution of vestibular (A) and visual (B) 3D rotation preferences. Each data point in the scatter plot corresponds to the preferred azimuth (abscissa) and elevation (ordinate) of (more ...)
As for translation, rotation tuning was typically broad, with tuning width showing no significant dependence on azimuth preference (). In comparison to area MSTd, we found that the average tuning width in VIP was significantly narrower for both the visual and vestibular rotation conditions (p<0.001, Wilcoxon rank-sum test).
Nearly all vestibular and visual rotation responses in VIP were classified as single-peaked (). As illustrated in , the distribution of the absolute difference in 3D preferred direction (|Δ Preferred direction|) between visual and vestibular rotation responses of multisensory cells was significantly bimodal (p=0.01, uniformity test; puni
=0.61, modality test). Thus, similar to translation responses, VIP neurons can show either congruent or opposite rotational preferences for visual and vestibular stimuli. As discussed further below, this property of VIP neurons differs from that seen in MSTd, where almost all neurons have opposite rotation preferences for visual and vestibular stimuli (Takahashi et al., 2007
Figure 7 Comparison of tuning preferences and direction selectivity between visual and vestibular responses of VIP neurons during rotation. (A) Distribution of the absolute 3D difference in preferred direction (|Δ Preferred direction|) between visual and (more ...)
Visual and vestibular rotation responses in VIP had similar tuning strength, as illustrated in (Wilcoxon matched pairstest, p=0.20, n=142). As for translation tuning (), the visual DDI of multisensory neurons (0.70 ±0.01) was greater, on average, than the visual DDI of unisensory neurons (0.68 ±0.01; p<0.001, Wilcoxon rank-sum test, , black vs. green symbols). A similar trend was seen for the vestibular DDI values, but the difference was not significant (p=0.21, Wilcoxon rank-sum test; , black vs. red symbols).
Comparison between translation and rotation responses
Since all cells tested with the rotation protocol were also tested with the translation protocol, a direct comparison between rotation and translation tuning is possible. The distribution of the absolute difference in 3D direction preference, |Δ preferred direction|, between vestibular rotation and vestibular translation conditions was not significantly different from uniform (p=0.20, uniformity test), although there was some tendency for vestibular translation and rotation preferences to differ by ~90° (). That is, cells that prefer lateral translation (0°, 180°) also tend to prefer roll rotation (± 90°), although the effect did not reach statistical significance.
Figure 8 Summary of differences in direction preference and tuning strength between rotation and translation. (A), (B) Histograms of the absolute differences in 3D preferred direction (|Δ preferred direction|) between rotation and translation for the vestibular (more ...)
The corresponding distribution of differences in direction preference between visual rotation and translation responses was significantly non-uniform (p<0.001, uniformity test) and unimodal (puni=0.88, modality test; mean: 90.0° ± 2.8; ). This relationship is not surprising when one considers that, at least for lateral/vertical optic flow (the preferred stimuli for many VIP cells; and ), visual translation and rotation preferences are typically linked by the two-dimensional visual motion selectivity of the cell. For example, a neuron that prefers leftward visual motion on the display screen will respond well to both a yaw rotation stimulus (azimuth 0°, elevation 90°) and a lateral (rightward) translation stimulus (azimuth 0°, elevation 0°). Note that these two stimulus directions are 90° apart on the sphere ().
Since |Δ preferred direction| is computed as the smallest angle between a pair of preferred direction vectors in 3D within the interval of (0, 180°), it is not known whether the observed peak near 90° in is derived from a single mode at −90° or from two modes at +90 and −90°. To examine this, we also illustrate the differences between translation and rotation preferences in each cardinal plane: front view, side view, and top view () over the entire 360° range. For the visual condition, the distribution of 2D direction differences in the frontal plane is more revealing than those in the other two planes: data are tightly clustered around −90°, with no cells having direction differences of +90°. Thus, the data from the visual condition are mostly consistent with the idea that the preferred directions for translation and rotation are related through the 2D visual motion selectivity of VIP neurons. Note that the visual fixation target is head fixed in this study, such that both yaw/pitch rotations and lateral translations produce laminar optic flow in which all elements move in the same direction on the display screen. Although the speed of dot motion varies with distance for translation (but not rotation), the argument here depends only on the direction preferences for rotation and translation and not on speed selectivity.
Figure 9 Comparison between VIP responses during fixation and in darkness. (A), (B) Distribution of the absolute difference in preferred direction for neurons with significant unimodal tuning during both fixation and in complete darkness; data are shown for translation (more ...)
Unlike in the visual stimulus condition, differences in direction preference between translation and rotation for the vestibular condition were not tightly distributed in any of the cardinal planes (), a finding that is similar to that reported previously for MSTd (Takahashi et al., 2007
), thalamus (Meng et al., 2007
) and vestibular nuclei neurons (Dickman and Angelaki, 2002
; Bryan and Angelaki, 2009
). In contrast, vestibular preferences for rotation and translation in PIVC were spatially coordinated such that each cell preferring a given translation direction also responded maximally to rotations about an axis that was roughly perpendicular to the translation preference (Chen et al., 2010
). For example, cells that preferred left-right translation also preferred roll rotation; and cells that preferred forward/backward translation tended to prefer pitch rotation. Thus, vestibular responses in VIP and MSTd differ from those in PIVC in that rotation/translation preferences are not as tightly aligned.
Tuning strength was not significantly different between vestibular translation (DDI = 0.62 ± 0.01, SE) and vestibular rotation (0.61 ± 0.01) conditions (p=0.27, Wilcoxon matched pairs test), as illustrated in . By comparison, the visual rotation DDI (0.65 ± 0.01) was less than the visual translation DDI (0.68 ± 0.01) on average, and this difference was significant (p=0.001, Wilcoxon matched pairs test, ).
Fixation vs. Darkness
A subpopulation of VIP cells was also tested during vestibular translation (n=36) and rotation (n=20) in complete darkness (with the video projector turned off; see Materials and Methods). As summarized in , spatial tuning and response selectivity were similar whether the animal fixated a head-fixed target on the screen or was moved in darkness. This conclusion is based on two comparisons. First, for responses with significant unimodal spatial tuning under both conditions (translation: 14/36 cells; rotation: 8/20 cells), the distribution of the absolute difference in direction preference between fixation and darkness was narrow and biased strongly toward zero (median = 12.3° for translation and 40.1° for rotation; ). With the exception of one cell, VIP neurons had similar (<60°) direction preferences in the fixation and darkness protocols. Second, tuning strength, as measured with the DDI, was not significantly different between fixation and darkness (Wilcoxon matched pairs test, p=0.09 for translation; p=0.39 for rotation), and DDI values for these two conditions were robustly correlated (, r=0.64, p<0.001 for translation, and r=0.52, p=0.02 for rotation). These data suggest that responses in our ‘vestibular’ condition mainly reflect sensory input from the vestibular apparatus, rather than either retinal slip or efferent eye movement signals. Note, however, that a somatosensory contribution to the vestibular tuning of VIP cells cannot be excluded.
Responses to combined visual/vestibular stimuli
In order to characterize the interaction between the two sensory modalities, a subset of neurons (50 cells for translation and 30 cells for rotation) was tested under three stimulus conditions (vestibular only, visual only and combined stimulation; see Materials and Methods). For congruent cells, the combined response had tuning that was similar to the single-cue responses, as shown for an example congruent cell in . In contrast, two different types of interactions were observed for opposite cells. For some cells, like the example in , the combined response was dominated by the vestibular tuning. For other cells, like that in , the combined response was dominated by the visual tuning.
Figure 10 Examples of 3D translation tuning for three VIP neurons tested in the vestibular (left), visual (middle) and combined (right) conditions. Format as in . (A) Tuning of a ‘congruent’ multisensory neuron. Vestibular condition: direction (more ...)
These observations are summarized in , which shows the distributions of |Δ Preferred direction| between combined and vestibular conditions () or between combined and visual conditions (). For congruent multisensory cells (black bars), |Δ Preferred direction| was generally very small, as expected, indicating that the direction preference in the combined condition was similar to those in the visual and vestibular conditions. For opposite multisensory cells (gray bars), |Δ Preferred direction| was broadly distributed, demonstrating that the direction preference of the combined response could be dominated by either the vestibular or visual preference. As expected, the combined tuning of unimodal cells (red/green bars) was similar to the unimodal responses. The fact that combined responses of multisensory VIP cells can be dominated by either the visual or vestibular tuning contrasts with previous results from area MSTd, for which combined responses under identical stimulus conditions (100% motion coherence) were generally dominated by the visual response (Gu et al., 2006
; Takahashi et al., 2007
; Morgan et al., 2008
Figure 11 Summary of the differences in direction preference and comparison of tuning strength between the combined condition and each of the vestibular and visual conditions. (A, B) Distributions of the absolute difference in 3D preferred direction (|Δ (more ...)
How does tuning strength of the combined response, as quantified by the DDI, compare with tuning strength of the single-cue responses? Across the population, combined responses to translation tend to have greater DDI values (0.71 ±0.02 SE) than vestibular (0.63 ±0.02 SE, p<0.001, Wilcoxon matched pairs test) or visual responses (0.68 ±0.02 SE, p=0.03). Stronger tuning was also seen during combined responses to rotation (0.68 ±0.02) than for vestibular (0.64 ±0.02, p=0.02, Wilcoxon matched pairs test) or visual (0.64 ±0.02, p=0.11) rotation responses, although the latter difference did not reach significance. Interestingly, visual-only and vestibular-only cells (open green and red symbols in ) also tend to show greater DDI (stronger tuning) during cue combination (translation: p<0.001 for visual-only and p=0.22 for vestibular-only; rotation: p=0.13 for visual-only and p<0.001 for vestibular-only, Wilcoxon rank-sum tests).
To explore further the relative contributions of vestibular and visual responses to the combined tuning of multisensory neurons, we fit the combined responses of each neuron with a weighted linear sum of the visual and vestibular responses (see Materials and Methods). The linear model generally provided very good fits to the combined responses. For translation, the median values of R2
are 0.91 for VIP, 0.87 for VPS and 0.92 for MSTd, respectively. For rotation, the median values of R2
are 0.78 for VIP and 0.97 for MSTd. Note that this is consistent with findings of a previous study of MSTd neurons (Morgan et al., 2008
), for which inclusion of a larger range of stimuli allowed comparison of linear and nonlinear models, with little explanatory power gained when including nonlinear terms. In addition, we only included cells with good fits of the linear model (R2
>0.7) in the following analyses. From these fits, we computed vestibular and visual gains, as well as the gain ratio (). These gains describe the weighting of visual and vestibular inputs to the combined response. A gain ratio of 1 indicates that vestibular and visual inputs are equally weighted in the combined response; a gain ratio<1 indicates that vestibular inputs contribute less than visual inputs; and a gain ratio>1 means that vestibular inputs outweigh the visual inputs.
Figure 12 Distributions of the gain ratio, describing the relative weighting of the visual and vestibular contributions to the combined response for (A) translation and (B) rotation. (A) Top row: data from area VIP (n=17); middle row: data from area MSTd (n=125); (more ...)
For translation, the mean gain ratio in VIP was 1.33 ± 0.79 (geometric mean mean ± SE), with roughly half of the neurons having gain ratios <1 and >1 (, top). This value was significantly greater than that for MSTd (0.33 ± 0.29, , middle) (p<0.001, Wilcoxon rank-sumtest), but not significantly less than that for VPS (2.29 ± 1.52, , bottom, p=0.14). A similar tendency was observed for responses to rotation, with a mean gain ratio of 1.05 ± 1.08 for VIP and 0.41 ± 0.92 for MSTd, although this difference did not quite reach significance due to the smaller samples (p=0.06, Wilcoxon rank-sum test). Note that we did not measure visual rotation responses for area VPS, hence no rotational gain ratios are presented for VPS. Overall, this analysis supports the findings of and , namely that vestibular cues contribute more strongly to combined responses in VIP than in MSTd, but less strongly than in VPS.
Unlike in MSTd (Takahashi et al., 2007
), there was no dependence of the gain ratio on the relative strength of visual and vestibular responses in VIP (measured by the ‘visual-vestibular ratio’, see Materials and Methods; Spearman rank correlation, p>0.3). That is, the relative tuning strength of the two single cues was not predictive of how the cues interact to determine the combined response in VIP.
Characterization of spatiotemporal dynamics
To assess the flow of visual and vestibular signals through a network of cortical areas involved in self-motion analysis, it is valuable to examine the timing of responses. However, response timing has to be evaluated while accounting for potential variations in response dynamics (e.g., velocity vs. acceleration coding) across areas. We have recently developed a model-based approach to this issue (Chen et al., 2011b
), and we apply it here to visual/vestibular responses in areas VIP, MSTd, and VPS.
Three different models, reflecting coding of velocity (model V), velocity + acceleration (model VA), or velocity + acceleration + position (model VAP), were fit to the spatiotemporal responses of each neuron (see Materials and Methods for details). To reduce dimensionality, each model was fit to two-dimensional data from the horizontal, frontal, and median planes of the spherical stimulus space (), provided that significant space-time structure was exhibited for each plane as described in Materials and Methods. We report results from the plane with the strongest response modulation for each neuron. In addition, we only quantified results from cells for which the goodness of fit of the model was high (R2>0.7) for the best response plane. For the vestibular condition, 30 VIP neurons, 48 MSTd cells, and 24 VPS neurons met these criteria. For the visual condition, 76 cells from VIP, 119 from MSTd and 14 from VPS met the criteria. Only 7 VIP neurons passed these criteria for the combined condition, hence we do not present fit results for that condition. Similarly, there was insufficient data from the rotation protocols for this analysis.
shows fits of models V, VA and VAP to visual responses from an example neuron in area VIP. Responses from this neuron were equally well fit by model V (), model VA () and model VAP (), as illustrated by the fit residuals. Indeed, the fits of the three models (red, green and blue traces in ) are highly overlapping, consistent with a velocity weight, wv = 0.825 for model VA and a position weight, wp = 0.031 for model VAP. For this neuron, model V accounts for 93.4% of the variance in the data (93.5% for model VA, 93.6% for model VAP) and thus provides a good description of the spatiotemporal response profile. Models VA and VAP are not justified given the increase in the number of parameters (p>0.36, sequential F test). For visual heading tuning, this pattern of results was observed for the majority of neurons in areas VIP and MSTd ().
Figure 13 Example fits of velocity (model V), velocity+ acceleration (model VA) and velocity + acceleration + position (model VAP) models to the spatiotemporal visual responses of a VIP neuron. (A) Direction-time plot showing how direction tuning evolves over the (more ...)
Summary of best fitting models (visual response)
To summarize the relative strengths of velocity, acceleration, and position components in the neural responses, we computed the ratio of acceleration and velocity weights, wa/wv, as well as the position weight, wp (see Methods). Cumulative distributions of these weights for each area are shown in . For the vestibular stimulus condition, the acceleration to velocity weight ratio, wa/wv, was significantly larger in VIP (geometric mean ± SE: 1.59 ± 0.86) than in MSTd (0.70 ± 0.72 SE) (p=0.004, Wilcoxon rank-sum test), but not significantly different from VPS where the sample size was rather small (1.15 ± 1.00 SE) (p=0.44). The position weight, wp, was very small for VIP (0.05 ± 0.55 SE) and VPS (0.07 ± 0.68 SE), but significantly greater for MSTd (0.14 ± 0.41 SE) than either VIP or VPS (p<0.001, Wilcoxon rank-sum tests). Thus, for vestibular responses to translation, activity in VIP (and VPS) reflects fairly balanced contributions of velocity and acceleration, with little position component. In contrast, vestibular responses in MSTd are more dominated by stimulus velocity, with a modest but more substantial contribution from position.
Figure 14 Population comparison of parameters of spatiotemporal model fits among VIP, MSTd and VPS. (A) Cumulative distributions of the ratio of acceleration to velocity weights (wa/wv) from model VA for the vestibular (left, VIP: n=30; MSTd: n=48; VPS: n=24) and (more ...)
For the visual stimulus condition, the ratio of acceleration and velocity weights (wa/wv) was relatively low (VIP: 0.22 ± 0.32 SE; MSTd: 0.17 ± 0.42 SE; VPS: 0.16 ± 0.66 SE), reflecting dominance of stimulus velocity, and there were no significant differences across areas (p>0.22, Wilcoxon rank-sum tests). The position weight was also low in VIP (0.04 ± 0.31 SE) and MSTd (0.05 ± 0.31 SE), but was slightly greater in area VPS (0.10 ± 0.88 SE) (p=0.009, Wilcoxon rank-sum test). Thus, visual heading responses were largely dominated by velocity in all three areas.
In addition to estimating the relative contributions of velocity, acceleration, and position signals to neural responses, the model-fitting analysis also allowed us to compute the overall latency of the response (parameter t0
in Eq. 6
), independent of the specific mixture of temporal response components needed to fit each neuron’s response. shows distributions of response delays, from the best-fitting model, for areas VIP, MSTd, and VPS. For the vestibular condition, response latency for VIP neurons (mean ± SE: 112 ± 26 ms) was significantly earlier than for MSTd neurons (193 ± 26 ms) (p=0.007, Wilcoxon rank-sum test), and not significantly different from that for VPS cells (66 ± 41 ms) (p=0.28). For the visual condition, mean response latencies for the three areas did not differ significantly from each other (VIP: 48 ± 18 ms; MSTd: 77 ± 19 ms; VPS: 26 ± 73 ms, p>0.16, Wilcoxon rank-sum tests). Thus, timing of vestibular responses in VIP was faster than in MSTd, whereas timing of optic flow responses did not differ significantly among areas.
Figure 15 Distributions of response latency, derived from model fits, for neurons in VIP (top row), MSTd (middle row) and VPS (bottom row), as tested under the vestibular (left, VIP: n=30; MSTd: n=48; VPS: n=24) and visual (right, VIP: n=76; MSTd: n=119; VPS: n=14) (more ...)
Selectivity as a function of location within the intraparietal sulcus
Visual-only, vestibular-only and multisensory cells were encountered throughout the anterior/posterior extent of VIP (). The relationship between DDI and anterior-posterior coordinates was examined for both the right and left hemispheres of monkey J () and the right hemisphere of monkey C (), as all three were extensively explored. There was a significant correlation between the DDI for vestibular translation and the anterior-posterior coordinates (monkey J’s left hemisphere: r=0.32, p=0.02; monkey J’s right hemisphere: r=0.20, p=0.09; monkey C’s right hemisphere, r=0.40, p<0.001). No such relationship was seen for the visual translation condition (p>0.5; , bottom panels). For rotation (likely due to the small sample of neurons), we did not see any significant correlations between DDI and the anterior-posterior location of electrode penetrations, and this was true for both the vestibular and visual conditions.
Figure 16 Relationship between tuning strength, as measured by DDI, and cell location within the intraparietal sulcus. DDI values for the vestibular condition (top row) and the visual condition (bottom row) are plotted as a function of the anterior-posterior stereotaxic (more ...)
As shown by the different colors in , multisensory neurons and unisensory neurons were intermingled in VIP and were found across the entire anterior-posterior extent in these animals. Similarly, we found all of these cell types independent of the medial-lateral location of the electrode penetration, and within the medial bank, lateral bank and tip of the intraparietal sulcus.