The sense of touch affords a remarkable sensitivity to the microstructure of surfaces, affording us the ability to sense elements ranging in size from tens of nanometers to tens of millimeters. The hand sends signals about texture to the brain using three classes of nerve fibers through two neural codes: coarse features in spatial patterns of activation and fine features in precise temporal spiking patterns. In this study, we show that these nerve signals culminate in a complex, high-dimensional representation of texture in somatosensory cortex, whose structure can account for the structure of texture perception. This complexity arises from the neurons that act as idiosyncratic detectors of spatial and/or temporal motifs in the afferent input.

In the somatosensory nerves, the tactile perception of texture is driven by spatial and temporal patterns of activation distributed across three populations of afferents. These disparate streams of information must then be integrated centrally to achieve a unified percept of texture. To investigate the representation of texture in somatosensory cortex, we scanned a wide range of natural textures across the fingertips of rhesus macaques and recorded the responses evoked in Brodmann’s areas 3b, 1, and 2. We found that texture identity is reliably encoded in the idiosyncratic responses of populations of cortical neurons, giving rise to a high-dimensional representation of texture. Cortical neurons fall along a continuum in their sensitivity to fine vs. coarse texture, and neurons at the extrema of this continuum seem to receive their major input from different afferent populations. Finally, we show that cortical responses can account for several aspects of texture perception in humans.

Our sense of touch endows us with an exquisite sensitivity to surface microstructure. We can perceive surface features that range in size from tens of nanometers (1) to tens of millimeters and integrate these to form a cohesive textural percept. In the somatosensory nerves, surface features at different spatial scales are encoded in different populations of afferents and rely on different neural representations. Coarse-surface features are reflected in the spatial patterns of activation evoked in slowly adapting type-1 (SA1) and rapidly adapting (RA) afferents, whose small receptive fields give rise to a faithful neural image of surface elements measured in millimeters (2, 3). However, many tangible surface features are too small and too close together to be encoded spatially because the spatial code is limited by the innervation density of the skin (4, 5). To perceive fine textural features requires movement between skin and surface, which leads to the elicitation of texture-specific skin vibrations, which in turn evoke precisely timed texture-specific spiking patterns in RA and Pacinian corpuscle-associated (PC) afferents (6⇓⇓⇓⇓–11). These spatial and temporal representations must be combined and synthesized to achieve a unified percept of texture, a process about which little is known.

While neurons in somatosensory cortex have been shown to encode information about texture, previous studies investigating cortical texture representations used surfaces with elements in the range of millimeters, such as Braille-like dot patterns (12, 13) and gratings (14⇓–16), which span only a small fraction of the wide range of tangible textures. We have previously shown that responses to such textures—which only engage the spatial mechanism—provide an incomplete view of the neural mechanisms that mediate the perception of texture (10, 11).

To fill this gap, we examined how textures that span the tangible range are encoded in somatosensory cortex. To this end, we scanned a wide range of textures—including fabrics, furs, and papers, in addition to the traditional embossed dots and gratings—across the fingertips of (awake) rhesus macaques and recorded the responses evoked in somatosensory cortex, including Brodmann’s areas 3b, 1, and 2. First, we found that texture identity is faithfully encoded by these neuronal populations and that texture information is distributed across neurons which each exhibit idiosyncratic texture responses. Second, we showed that the heterogeneity across somatosensory neurons is in part driven by differences in the submodality composition of their input (SA1, RA, and PC). We then discovered the downstream recipients of the spatial and temporal codes observed at the periphery: A subpopulation of cortical neurons receives strong input from SA1 fibers and preferentially encodes coarse textural features, whereas another population of neurons receives strong input from PC fibers and preferentially encodes fine surface features. Finally, we showed that the responses of somatosensory neurons account for psychophysical reports of texture obtained from human observers.

Results

We recorded the activity evoked in 141 neurons in somatosensory cortex (35 from area 3b, 81 from area 1, and 25 from area 2) from three rhesus macaques with receptive fields on the distal fingertip, as each of 59 textured surfaces (SI Appendix, Table S1) was scanned across the skin by using a rotating drum stimulator, which allowed for precise control of scanning speed and indentation depth (Fig. 1 A and B). These surfaces were chosen to vary widely in microstructure and material properties in an attempt to explore as fully as possible the range of everyday textures. The objective of the study was to determine the degree to which texture information is encoded in cortex, examine the nature of this representation, and assess the degree to which this representation can account for perception.

Fig. 1. Experimental apparatus and sample texture responses. (A) Textures were passively presented to the distal finger pads of awake macaques. (B) The rotating drum stimulator—on which the 59 textures were mounted—allows a surface to be scanned across the fingertip at a precise and repeatable speed and depth of indentation into the skin. (C) Sample spiking responses of five neurons each in areas 3b, 1, and 2 to five repetitions of eight textured surfaces.

Neurons in Somatosensory Cortex Encode Texture. First, we examined the degree to which the responses of individual somatosensory neurons were modulated by texture (Fig. 1C). We found that nearly every neuron responded to at least one texture (140 of 141 neurons modulated above baseline firing rate; P < 0.05, permutation test with Bonferroni correction) and that each texture significantly modulated the response of at least 20% of the neurons (0.23–0.72–92, minimum–median–maximum proportion of textures across cells, permutation test). To test whether these neurons carry texture-specific information, we built a simple linear classifier based on single-trial spike counts. Nearly all neurons yielded classification performance that was significantly above chance (mean ± SD of performance: 6.7 ± 3.7%, chance performance: 1.7%, 95% of neurons > chance), and neurons that yielded better than chance performance were approximately equally prevalent in areas 3b, 1, and 2 (97%, 96%, and 88%, respectively; SI Appendix, Fig. S1A). Next, we examined the degree to which texture identity is encoded in the responses of populations of somatosensory neurons (Fig. 2A). To this end, we implemented the texture classifier using the responses of groups of neurons of varying size. We found that high-classification performance could be achieved with a small population of somatosensory neurons (as few as 83 neurons yielded 97% performance) and that the full population yielded nearly perfect performance (Fig. 2B). Classification performance was largely comparable across cortical modules (an average of 73%, 72%, and 62% for groups of 25 neurons in areas 3b, 1, and 2, respectively; SI Appendix, Fig. S1B) and was robust to (simulated) noise correlations (SI Appendix, Fig. S2A). In summary, small populations of somatosensory neurons convey sufficient information to support texture identification for a large and diverse texture set. Fig. 2. Neurons in somatosensory cortex encode texture. (A) The mean firing rate (across repetitions) evoked by each of 59 textures (columns) in somatosensory neurons, split by cortical field. Firing rates are normalized within neurons for display purposes, ranging from low (blue) to high (yellow). Textures are sorted according to the first principal component of the population response from lowest to highest. Cells are ordered first by area, then by variance of their firing rates across textures. Somatosensory neurons exhibit heterogeneous responses to textures. (B) Texture-classification performance of groups of cortical neurons vs. the size of the neuron group. As expected, classification performance improves as more cells are included. Gray dots denote the performance of individual neuronal groups, and the black trace denotes the mean as a function of group size. Groups of 66 cells, marked by the gray dashed line, yield a near-asymptotic performance of 97%.

The Cortical Representation of Texture Is High-Dimensional. Two factors drive the ability of neural populations to classify stimuli more accurately than do individual cells. First, as more neurons are included, the trial-to-trial variation in response is averaged out. Second, increasing the variety in tuning properties in the neural population can more effectively represent the high-dimensional character of a complex stimulus, and thus increase the effective dimensionality of the resulting neural representation. That is, insofar as different neurons respond to different aspects of a surface, these idiosyncratic responses will provide information beyond that available from simply averaging responses across cells. We examined the dimensionality of texture responses—the degree to which somatosensory neurons respond heterogeneously to texture—by performing a principal components analysis (PCA) on the population response. That is, we first characterized the correlational structure in texture responses across neurons in somatosensory cortex and then assessed the degree to which responses could be reduced to a smaller set of nonredundant signals. We found that most of the variance in neuronal responses was explained by the first principal component (Fig. 3A) (65% proportion of variance explained from the first component, essentially the mean population firing rate; R2 = 0.99), a signal that was strongly preserved across all three cortical areas [with intercorrelations of first principal components across pairs of areas all >0.95 (SI Appendix, Fig. S1C)]. As discussed below, this prominent neuronal dimension has a clear perceptual correlate. Fig. 3. The cortical representation of texture is high-dimensional. (A) Cumulative scree plot (proportion of variance explained) for the PCA on the population response to texture. The bulk of the response variance is carried by the first few components. (B) Texture classification (as in Fig. 2) using the cortical population response with the full population response (gray line) and with the first principal component removed (black line). The red dotted line denotes chance performance. Classification performance is only slightly reduced when this first component is removed. (C) Average classification performance of each individual principal component when distinguishing between pairs of textures. Responses were above chance, even for components that explained only a small proportion of the total variance. Error bars represent the SD across texture pairs and shuffles of the training and test sets. (D) Classification performance based on firing rates projected onto a subset of principal components, built by excluding the n principal components in decreasing order of their eigenvalues (i.e., removing the largest components first). Error bars denote the SD across shuffles of the training and testing sets. Even when dozens of the high-variance principal components are removed from the response, texture classification is still above chance. We sought to characterize whether heterogeneity in texture responses across neurons provides texture-specific information beyond that found in the mean population response. To this end, we again implemented the texture classifier, this time using only a subset of the principal components of the neural response. When the population response was collapsed onto a single dimension—the first principal component—classification performance dropped to 41%, compared with 99.4% when the entire response was used. Conversely, if we removed only the first principal component from the population response and preserved all other components, we achieved 92% classification accuracy with as few as 83 cells and 97% accuracy with the full population of 141 cells (Fig. 3B). In other words, the heterogeneity of neural responses to texture is a major contributor to the texture signal in cortex. Given the importance of low-variance dimensions to classification performance, we sought to assess how many of these dimensions are reliably informative about texture identity. To this end, we first quantified how many dimensions identified through PCA reliably carried texture information. We found that the first 30 dimensions carried sufficient texture information to distinguish pairs of textures (Fig. 3C; 95% of trial shuffles yielded above chance performance). We then examined the degree to which the response retained information about texture when multiple principal components were cumulatively removed (Fig. 3D). We found classification performance to be well above chance, even after removing 33 principal components (95% of trial shuffles yielded above-chance performance). Because the outcome of these analyses may depend on the structure of the trial-to-trial variability in the response, we verified that the measured dimensionality was robust to (simulated) noise correlations (SI Appendix, Fig. S2 B–D). Finally, because PCA does not necessarily identify the most informative dimensions of response, we implemented a recently developed measure of dimensionality which is not based on explained variance (like PCA) but, rather, gauges the ability of the response to reliably divide up the stimulus space [cf. Rigotti et al. (17); Materials and Methods]. Using this method, we found that the cortical response to texture can consistently classify split groups of up to 22 textures, suggesting that the texture representation in somatosensory cortex is at least 21-dimensional (SI Appendix, Fig. S2 E–I). Furthermore, this measurement of 21 response dimensions is likely an underestimate: Our classifier-based estimate of dimensionality is not only capped by the dimensionality of the neuronal representation, but also by the size of the stimulus set and of the recorded neuronal population (17). Indeed, we find that the dimensionality is still rapidly increasing as a function of neuronal group size for 141 cells (SI Appendix, Fig. S2F), so more neurons would likely yield an even higher-dimensional representation in response to our texture set. In total, these classification results suggest that the dimensionality of the neural representation is driven by a large number (dozens) of components which, while often only accounting individually for a small fraction of the overall response variance, nonetheless carry significant texture information.

Some Heterogeneity in Cortical Responses Can Be Attributed to Differences in Submodality Input. Next, we examined the degree to which the cortical response inherits its structure from the periphery, where texture signals are carried by three classes of low-threshold tactile nerve fibers. To this end, we leveraged previously obtained recordings of afferent responses (from 17 SA1, 15 RA, and 7 PC fibers) to a subset of 24 textures also used in the present study (10). We then evaluated, using multiple regression, the extent to which the mean population firing rate of SA1, RA, and PC afferents evoked by these 24 common textures could account for the firing rates of individual cortical cells, using the resulting standardized regression coefficients as a gauge of the relative similarity of each tactile submodality to each cortical neuron. First, we found that the different cortical neurons received their strongest input from different classes of tactile nerve fibers (44.7%, 37.6%, and 17.7% of neurons showed maximum regression coefficients from SA1, RA, and PC afferents, respectively). Second, the responses of individual somatosensory neurons implied submodality convergence, as reflected by the fact that many cortical neurons were significantly better explained by a combination of multiple afferents than they were by any single afferent (F test: 28% of cells better explained by all three coefficients than any single coefficient, P < 0.05). Because this test has low statistical power given the small number of common stimuli between the peripheral and cortical datasets, we also examined the adaptation properties of cortical neurons [that is, the dynamics of their responses to trapezoidal skin indentation (19)]. We found that many neurons (69%) showed both significant responses during the sustained portion of the indentation, indicative of SA1 input, as well as significant responses upon the removal of the probe, indicative of RA or PC input (SI Appendix, Fig. S3 A–D). Overall, 80% of neurons displayed submodality convergence by one or both of these measures. Thus, even at the single-neuron level, the texture representation in somatosensory cortex is built from signals integrated across tactile submodalities. Next, we examined what aspects of the high-dimensional texture representation in somatosensory cortex were inherited from structure in its peripheral inputs. To this end, we recalculated our PCA on both the peripheral and cortical population responses to their shared set of 24 textures. Using canonical correlation analysis (Materials and Methods), we found that the first three dimensions of the peripheral firing rates were significantly predictive of their cortical counterparts, but dimensions beyond these three did not yield better predictions (Fig. 4A). Within this shared space, the first principal axis in the cortex was highly correlated with its peripheral counterpart (r = 0.93). The second principal axis in the cortex was also correlated with its counterpart in the periphery (r = 0.89), and this axis separated neurons with strong SA1 input (and, to a lesser extent, RA input) from those with strong PC input. Indeed, the correlation between the weight of the second principal axis in the cortex and the SA1, RA, and PC regression coefficient was −0.43, −0.16, and 0.76, respectively. Furthermore, neurons that received strong PC input tended to produce texture responses that were correlated with each other but uncorrelated with the responses of neurons driven primarily by SA1 or RA responses (Fig. 4B), reflecting the stark difference in response properties of these two sources of input. Interestingly, the most strongly PC-like cells were predominantly located in area 1 (10 of 12 of cells with normalized PC weight > 0.8; the other 2 were in area 2; SI Appendix, Fig. S1D). Thus, the second dimension of variance in the cortical response has, at one extreme, SA1-like neurons and, at the other extreme, PC-like ones. The third principal axis in the cortex also showed correlation with its peripheral counterpart (r = 0.82), but its meaning is unclear. Although the first few principal axes of the texture representation in the cortex are inherited from the periphery, much of the structure in the cortical representation beyond these axes cannot be explained straightforwardly from the relative strengths of SA1, RA, and PC input. Fig. 4. Some heterogeneity in cortical responses can be attributed to differences in submodality input. (A) Strength of the prediction of cortical responses from the first n principal components of the peripheral texture response (implemented by using canonical correlation analysis; Materials and Methods). Beyond the first three principal components, performance declines due to overfitting. (B) Correlation matrix of texture-elicited firing rates with each row and column corresponding to a different neuron (cells with mean texture response > 40 Hz, n = 74). Cells are ordered by their PC regression weight, from least PC-like (lower left) to most PC-like (upper right). The red line divides neurons with PC regression weights greater than or less than 0.5. The most PC-like cells in somatosensory cortex tend to cluster because their texture-evoked firing rates are distinct from those of other neurons.

Neurons in Somatosensory Cortex Encode Textural Features at Different Spatial Scales. At the periphery, texture-specific surface features are encoded through multiple mechanisms. Coarse surface features—measured in millimeters—are primarily encoded in the spatial pattern of activation across of SA1 fibers (20) [and perhaps RA fibers as well (11)]. In contrast, fine surface features—typically measured in the tens or hundreds of micrometers—drive characteristic vibrations in the skin during texture scanning (9, 21, 22). These vibrations (and, by extension, textural features) are encoded in precisely timed, texture-specific temporal patterns in RA and PC fibers (10). Next, then, we sought to examine how these peripheral codes for texture were reflected in cortical responses. First, we tested the hypothesis that a subpopulation of somatosensory neurons act as spatial filters, well suited to extract information about coarse textural features, as has been proposed (12, 23). We also wished to assess the spatial scale over which such a mechanism might operate. To this end, we first characterized the spatial receptive fields of somatosensory neurons using well-established techniques (SI Appendix, Fig. S4 A–C). By using this approach, neurons have been shown to encode spatial features with excitatory subfields flanked by inhibitory ones (12), analogous to simple cells in the primary visual cortex (24). Consistent with previous reports, the measured receptive fields exhibited well-defined excitatory subfields (average 12.7 mm2, range 3.1–37.4 mm2) and inhibitory subfields (average 12.8 mm2, range 0–42.6 mm2). Inhibitory subfields tended to lag behind excitatory subfields along the scanning direction (62 of 67, or 93%, average 2.5 mm lag) (SI Appendix, Fig. S4D). Importantly, the spatial period of the subfield—that is, the distance between the excitatory and inhibitory subfields—spanned a range from 2 to 4 mm (SI Appendix, Fig. S4E). Thus, the spatial structure of cortical receptive fields is well suited to extract information about coarse features, but not fine ones. Note that this receptive field structure is ideal for computing the spatial derivative of the neural image, which has been shown to drive perceived roughness of coarsely textured surfaces (11, 18, 20). Counterintuitively, while PC fibers have substantially larger receptive fields than do SA1 or RA fibers, this tendency was not reflected in their cortical targets. Indeed, the receptive fields of PC-like neurons were of similar size as their SA1- or RA-like counterparts (excitatory subfield size: average 12.1 mm2; inhibitory subfield size: average 13.1 mm2; average 2.3 mm lag at 80 mm/s; SI Appendix, Fig. S4 F and G). Next, we examined the cortical manifestation of the temporal code for fine textural features carried at the periphery by RA and PC fibers. A characteristic feature of PC (and to some extent RA) responses to texture is the elicitation of high-frequency spiking patterns (>50 Hz) that are highly informative about texture identity, as these patterns reflect the succession of fine textural elements moving across their receptive fields (10). To explore the presence of such timing signals in the responses of somatosensory neurons, we designed two finely textured 3D patterns—gratings with spatial periods of 0.5 and 1 mm—to elicit skin vibrations at 160 and 80 Hz, respectively (given a scanning speed of 80 mm/s). We anticipated that these highly periodic components would be readily identifiable in the cortical responses and might encode fine textural features. We found that a subpopulation of somatosensory neurons produced phase-locked responses to these and other fine textures (SI Appendix, Fig. S3E), providing a strong analog to the temporal code observed at the periphery. As expected, phase-locked responses were stronger among somatosensory neurons with PC-like responses than among their SA1-like counterparts (Fig. 5B and SI Appendix, Fig. S3E). Indeed, while the spiking patterns of both sets of neurons consistently reflected the periodic structure of coarse features, PC-like responses much more reliably reflected the periodic structure of fine features, even if these were embedded among coarse features. Neurons with PC input are thus well suited to convey information about fine textural features. Fig. 5. Neurons in somatosensory cortex encode textural features at different spatial scales. (A) Spiking responses of seven SA1-like neurons (green; cells with SA1 regression coefficient > 0.5, n = 53) and seven PC-like neurons (orange; cells with PC regression coefficient > 0.5, n = 23) in response to five repeated presentations of three different textures: dots spaced 7.7 mm apart, a 1-mm-period grating, and a superposition of the dots with the grating. SA1-like responses exhibit strong entrainment to the coarse component of the texture (dot pattern). PC-like cells are more strongly driven by the fine component of the texture (grating). (B) Mean amplitude spectrum of the spiking responses of SA1-like (green) and PC-like (orange) cells to the same three textures as in A. PC-like cells exhibit high-frequency phase-locking to the temporal period of the grating (80 Hz), even when the dots are present, whereas SA1-like cells do not. (C) Discriminability (d′) of nine 3D-printed textures based on the firing rates they evoke in SA1- and PC-like neurons (green and orange, respectively). Error bars denote the bootstrapped SEMs across cells and texture pairs. While PC-like cells are sensitive to both coarse and fine features, SA1-like cells are sensitive only to coarse ones. In light of these observations, we wished to assess the respective abilities of these two subpopulations of neurons—SA1- and PC-like—to convey information about fine and coarse features. To this end, we examined the responses of these two neuronal populations to nine 3D-printed surfaces (SI Appendix, Fig. S5) in which coarse and fine features were parametrically combined (Fig. 5 A and B). We found that SA1-like neurons responded significantly more strongly to textures with coarse features than without, exhibiting only weak firing-rate modulation to the presence of fine features (20.7 vs. 2.40 spikes per s for coarse vs. fine, respectively; P < 0.001, paired t test). Conversely, PC-like neurons responded more strongly to textures with fine features than to those without (15.9 vs. 2.0 spikes per s, for fine vs. coarse, respectively; P < 0.01), and their rates were nearly independent of the presence or absence of coarse features. As might be expected, these differences in sensitivity to coarse and fine textures led to corresponding differences in the ability of individual cortical neurons to discriminate pairs of textures (measured by using a standard sensitivity index, d′). SA1-like responses were significantly better at discriminating coarse features—independent of fine features—than were their PC-like counterparts (P < 0.05, permutation test), and PC-like neurons were significantly better at discriminating fine features—independently of the coarse features—than were SA1-like neurons (P < 10−4, permutation test) (Fig. 5C). In conclusion, then, different subpopulations of somatosensory neurons preferentially encode textural features at different spatial scales.