The Fine Structure of Shape Tuning in Area V4

Transcription

1 Neuron, Volume 78 Supplemental Information The Fine Structure of Shape Tuning in Area V4 Anirvan S. Nandy, Tatyana O. Sharpee, John H. Reynolds, and Jude F. Mitchell Inventory of Supplementary Items Figure S1: details of analysis shown in Fig 1 Figure S2: further example neurons (as in Figs 2 and 3) Figure S3: further example neurons (as in Figs 2 and 3) Figure S4: details of analysis shown in Fig 5 Figure S5: details of analysis shown in Fig 7 Figure S6: control experiment figure Figure S7: control experiment figure

2 a III spikes/s spatial z-score shape z-score IV b shape tuning c corr=.1 (p=.36), n=8 d corr=.3 (p=.8), n=8 orientation tuning orientation tuning index shape tuning index average shape preference average shape preference Supp Fig 1. Selectivity and Tuning Indices, Related to Fig 1. (a) Firing rates (left column), spatial z-scores (Z space, see Methods; middle column) and shape z-scores (Z shape, see Methods; right column) for 2 example neurons (neurons III and IV in Fig 2). The firing rate and shape z-score maps are shown for all composite stimuli in the 5x5 location grid. Significant spatial locations are marked with x in the middle column. Among these spatially significant locations, those that are significantly shape selective are marked with x in the right column. Both neurons are significantly spatially selective, but neuron IV is not shape selective. (b) Schematic illustrating the axes in stimulus space along which two tuning indices orientation and shape were calculated. (c) Scatter-plot of orientation tuning index (OTI) versus the average shape preference for all shape selective neurons (n=8). The OTI was calculated in terms of the circular variance of the neuronal responses evaluated at the preferred shape category at the maximally responsive spatial location for each neuron. The two variables are not correlated. (d) Scatter plot of shape tuning index (STI) versus the average shape preference for all shape selective neurons (n=8). The STI was calculated as the difference between the maximum and minimum z-scored responses evaluated at the preferred shape orientation at the maximally responsive spatial location for each neuron. STI is not correlated with shape preference.

3 j_51_u1 j_43_u1 j_42_u5 j_59_u spikes/s ρ model Supp Fig 2. Examples of neurons exhibiting spatially invariant tuning to straight or very low-curvature contours, Related to Figs 2-3. Top row: smoothed fine-scale orientation map with hue indicating preferred orientation, saturation indicating sharpness of orientation tuning and value indicating average response (see Fig 6 for hue-saturation-value colorcoding description). Middle row: Location-specific shape or set of shapes to which the neuron responded preferentially (greater than 9% of local peak rate). Shapes are spatially superimposed at each grid location. Bottom row: Model correlations (full model) at each spatially significant location in the RF. The spatial locations where the model correlations are significant (compared to spatially shuffled arrangements, see Methods and Supp Fig 5a-b) are demarcated with x (same format as in Fig 7a).

4 j_35_u4 j_29_u3 m_12_u14 j_67_u spikes/s ρ model Supp Fig 3. Examples of neurons exhibiting spatially varying shape tuning, Related to Figs 2-3. Same format as in Supp Fig 2.

5 I II Locationspecific response maps Example pairs empirical distribution ρ σ Pair-wise pattern correlation ρ Supp Fig 4. Analysis of spatial invariance, Related to Fig 5. The method for assessing translation invariance is illustrated for two example neurons. Top row: Location specific response maps in the 5x5 response grid. Middle row: The empirical distribution of correlation coefficients between the response patterns at a pair of spatially significant locations (see Methods) is shown for a couple of location pairs for each example neuron. These are the same two example neurons I and II in Figs 2 and 3. Bottom row: The undirected graphs show the pattern correlation (expected value of the empirical distributions above) for all possible location pairs with significant response. Warmer colors indicate that the location pairs have similar response patterns, while cooler colors indicate dissimilar patterns.

6 a c space only fine-scale map original shuffle 1 shuffle 2 shuffle 3 measured sign + - b RF I II III ρ model orientation only 2 RF location: 1 2 RF location: 1 2 RF location: p= model RF location: 2 null correlation ρ distribution model ρ null-model p= p= RF location: 2 p= p= RF location: 2 p= RF location: RF location: 3 p= RF location: 4 p= RF location: 3 p= RF location: 4 p= p= RF location: 4 p= RF location: 5 p= RF location: 5 p= RF location: 6 p= RF location: 5 p= RF location: 6 p= RF location: 6 p= RF location: 7 p= Supp Fig 5. Distribution of correlations between measured and predicted response patterns, Related to Fig 7. (a) Illustration of the spatial shuffling procedure used to determine the statistical significance of the predicted responses. For example neuron II of Figs 2 and 3, the fine-scale orientation map is shown on the left panel. The other panels show 3 spatially shuffled versions of the fine-scale orientation map. The shuffling is illustrated only for the coarse-grid location highlighted in red. Note that only the positions of the underlying 3x3 fine-scale grid are spatially permuted. (b) For the three example neurons in Figs 2 and 3 (columns) and for each spatially significant response location (rows): the gray histogram shows the distribution of null correlations i.e. the correlation coefficients between the measured response pattern to the composite shapes and those predicted from spatially shuffled versions of the fine-scale orientation map (ρ null-model, see Methods); the red line indicates the model correlation i.e. the correlation between the measured response pattern and the response pattern predicted from the fine-scale orientation map (ρ model, see Methods). All data are for the full model only. Also indicated are the p-values of the significance calculation of the model correlations at the.5 significance level (Bonferroni corrected for multiple comparisons). (c) Illustration of fine-scale maps used for the two reduced versions of the pooling model. An example segment of a fine-scale map is shown on the left. The space-only version (upper panel) was obtained by averaging across orientation at each fine-grid location. These maps do not have any local orientation tuning. The orientation-only version (lower panel) was obtained by subtracting the average orientation response (as in the space-only version) from the measured data at each fine-grid location. These maps do not contain any local spatial information. In the orientation-only maps, the line lengths are proportional to magnitude, while the color indicates sign (blue for positive, red for negative).

7 a j_12_u1 b j_11_u1 c j_92_u SLOW FAST FAST SLOW spikes/s d j_91_u1 e j_97_u1 f j_13_u Supp Fig 6. Neurons exhibit virtually identical tuning irrespective of stimulus duration. (a-f) Six example neurons tested with both the fast reverse correlation procedure (16 ms stimulus duration) and a slow reverse correlation procedure (2 ms duration). Top panel: Average time course for the fast (red) and slow (blue) trials +/- s.t.d. The mean firing rate was calculated using a temporal window (determined independently for each neuron and for each response category i.e. fast and slow), where the firing rate exceeded the baseline rate (calculated from a temporal window between and 2 ms after stimulus onset) by 4 standard deviations. For the slow presentations, the temporal kernel included the sustained (if any) and offset responses. The red and blue bars mark the temporal window over which the mean firing rate was calculated for the fast and slow stimuli respectively. Lower-left panel: Average response map for the fast presentations (averaged across all spatially significant RF locations). Lower-right panel: Average response map for the slow presentations.

8 a j_76_u1 b j_63_u1 m_17_u3 m_17_u8 GABOR SCRAMBLED Supp Fig 7. Tuning for Gabor composites and scrambled stimuli. (a) Average response maps (averaged across all spatially significant grid locations) for an example neuron where the composite stimuli were composed of bars (left panel) or Gabors (right panel). (b) Comparisons of average response maps (averaged across all spatially significant grid locations) for 3 example neurons for the composite stimuli (left panels) versus spatially scrambled versions (right panels) of the composite stimuli.

9 SUPPLEMENTAR METHODS Electrophysiology Neurons were recorded in area V4 in two rhesus macaques. Experimental and surgical procedures have been described previously (Reynolds et al., 1999). All procedures were approved by the Institutional Animal Care and Use Committee and conformed to NIH guidelines. A recording chamber was placed over the prelunate gyrus, on the basis of preoperative MRI imaging. At the beginning of the study, several recordings were made at different positions in each recording chamber to ensure that the electrode was in area V4, on the basis of RF sizes, topographic organization, and feature preferences. To inhibit granulation tissue growth in the chamber, the antimitotic 5 Fluorouracil (5FU) was applied three times each week (Spinks et al., 23). In each recording session, one to four tungsten electrodes (FHC) were advanced into the cortex using a multielectrode drive (NAN 4-tower drive, Plexon Inc., or 3NRM-3A microdrive, Crist Instruments). Neuronal signals were recorded extracellularly, filtered, and stored using the Multichannel Acquisition Processor system (Plexon, Inc). The raw field potential was filtered from 4 Hz to 8.8 khz. To isolate spikes, a threshold was set manually based on the amplitude of the noise. The timestamp and waveform of each threshold-crossing event were saved. We set the threshold low enough to ensure some fraction of noise events were saved so we could later distinguish to what extent isolated units were separated from the noise distribution. Waveforms were sampled at 4 khz (25 µs/sample), and an 8 µs trace of each waveform was stored that extended from 2 µs before to 6 µs after the crossing of a negative threshold. For each neuron, all recorded action potentials were aligned to their troughs. We used the trough to align waveforms because it was generally the sharpest feature of the waveform and was thus less sensitive to low-amplitude noise than the peak of the waveform. Single units were isolated in the Plexon Offline Sorter based on waveform shape and were included only if they formed an identifiable cluster, separate from noise and other units, when projected into the principal components of waveforms recorded on that electrode. Data analysis Orientation Tuning Index (OTI) and Shape Tuning Index (STI): For each shape selective unit we calculated two tuning indices one along the dimension for orientation and the other along the dimension for shape (Supp Fig 1b). The orientation-tuning index (OTI) was calculated as one minus the circular variance of the neuronal responses (or equivalently the resultant of the responses in the orientation domain, r ˆ k e iθ k ˆ ) evaluated at the preferred shape k category at the maximally responsive spatial location for each neuron. Note that there were 8 unique orientations for the straight shapes, while there were 16 for the other categories. The shape-tuning index (STI) was calculated as the difference between the maximum and minimum z-scored responses evaluated at the preferred shape orientation at the maximally responsive spatial location for each neuron. Neither OTI nor STI are correlated with average shape preference (Supp Fig 1c-d), indicating that neurons selective for medium and higher curvature were no different from straight/low curvature neurons in terms of sharpness of feature tuning. Control experiments We used several variations of the basic experimental procedure in three control experiments. In the first control experiment, the composite stimuli were presented in both a fast (as in the main experiment) and a slow reverse correlation procedure (Supp Fig 6). For the slow trials, the stimuli were presented for 2 ms as compared to the 16 ms duration used in the main r k k 2

10 experiment. The temporal kernel was selected from a window between 6 and 3 ms after stimulus onset using the same criteria as in the main experiment. The slow stimuli were presented in the central 3x3 grid locations of the coarse 5x5 location space. In the second control experiment, we used Gabors (3.33 cycles/deg, aspect ratio 12:5, size matched to oriented bars) instead of oriented bars as components of the composite shapes in 5% of trials (Supp Fig 7a). Finally, in the third control experiment, we used spatially scrambled versions of the composite shapes in 5% of trials. The spatial scrambling was done by preserving the location and orientation of the central element of a composite shape, while the end elements were randomly rotated about the center while preserving their respective orientations and their center-to-center distance from the central element (Supp Fig 7b). This scrambling is conceptually similar to our description of local spatial shuffling in the null hypothesis test for Fig 7. It tests against the possibility of the neurons responding to component orientations irrespective of their spatial alignment. REFERENCES Reynolds, J. H., Chelazzi, L., and Desimone, R. (1999). Competitive mechanisms subserve attention in macaque areas V2 and V4. J Neurosci, 19(5), Spinks, R. L., Baker, S. N., Jackson, A., Khaw, P. T., and Lemon, R. N. (23). Problem of dural scarring in recording from awake, behaving monkeys: a solution using 5-fluorouracil. J Neurophysiol, 9(2),