Skip to main content
  • Loading metrics

Population coding strategies in human tactile afferents

  • Giulia Corniani ,

    Contributed equally to this work with: Giulia Corniani, Miguel A. Casal

    Roles Conceptualization, Formal analysis, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

    Affiliations Active Touch Laboratory, Department of Psychology, University of Sheffield, Sheffield, United Kingdom, Sheffield Robotics, University of Sheffield, Sheffield, United Kingdom

  • Miguel A. Casal ,

    Contributed equally to this work with: Giulia Corniani, Miguel A. Casal

    Roles Conceptualization, Formal analysis, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

    Affiliations Department of Computer Science, Bioengineering, Robotics and Systems Engineering, University of Genova, Genova, Italy, Laboratory of Neural Computation, Istituto Italiano di Tecnologia, Genova, Italy

  • Stefano Panzeri ,

    Roles Conceptualization, Funding acquisition, Supervision, Writing – review & editing (SP); (HSP)

    Affiliations Laboratory of Neural Computation, Istituto Italiano di Tecnologia, Genova, Italy, Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology Hamburg (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany

  • Hannes P. Saal

    Roles Conceptualization, Funding acquisition, Supervision, Writing – review & editing (SP); (HSP)

    Affiliations Active Touch Laboratory, Department of Psychology, University of Sheffield, Sheffield, United Kingdom, Sheffield Robotics, University of Sheffield, Sheffield, United Kingdom


Sensory information is conveyed by populations of neurons, and coding strategies cannot always be deduced when considering individual neurons. Moreover, information coding depends on the number of neurons available and on the composition of the population when multiple classes with different response properties are available. Here, we study population coding in human tactile afferents by employing a recently developed simulator of mechanoreceptor firing activity. First, we highlight the interplay of afferents within each class. We demonstrate that the optimal afferent density to convey maximal information depends on both the tactile feature under consideration and the afferent class. Second, we find that information is spread across different classes for all tactile features and that each class encodes both redundant and complementary information with respect to the other afferent classes. Specifically, combining information from multiple afferent classes improves information transmission and is often more efficient than increasing the density of afferents from the same class. Finally, we examine the importance of temporal and spatial contributions, respectively, to the joint spatiotemporal code. On average, destroying temporal information is more destructive than removing spatial information, but the importance of either depends on the stimulus feature analyzed. Overall, our results suggest that both optimal afferent innervation densities and the composition of the population depend in complex ways on the tactile features in question, potentially accounting for the variety in which tactile peripheral populations are assembled in different regions across the body.

Author summary

Touching an object elicits neural responses from hundreds or thousands of individual tactile receptors of different classes embedded within our hand. Information about the extent of contact, the strength of the touch, and its temporal profile are carried jointly in this population response to be processed further by the central nervous system. However, studying the nature of the population code is empirically challenging, as electrophysiological recordings are typically obtained from single or a small number of neurons at most. Here, we make use of a computer simulation to recreate the population activity of large numbers of tactile neurons and examine how information is spread across different neurons. We find that tactile information increases with afferent density, but the saturation point depends on both the tactile feature and afferent class. Importantly, information is generally spread across multiple afferent classes, such that a combination of afferents from multiple classes yields higher information than the same number of neurons from a single class. These results will be useful to guide future experiments and theoretical work on the processing of tactile information by the central nervous system.


The brain processes information and makes decisions based on the activity of populations of neurons [1]. Studying population activity can reveal aspects of the neural code that are obscured when only individual neurons are considered [2]. For example, the well-known population vector technique has shown that the direction of arm movements can be precisely decoded from a population of cortical motor neurons, even though individual neurons are only broadly tuned to direction [3]. Moreover, some coding strategies will become evident only if the responses of multiple neurons are considered. For example, while a neuron that remains silent to a certain stimulus might not appear to convey any information at all, when it is part of a larger population where other neurons are responding, this silence can be meaningful [4]. Response correlations between neurons also affect decoding (see [5] for an example). Furthermore, populations often consist of heterogeneous classes of neurons, especially in sensory systems, such as the diversity of retinal ganglion cells in the visual pathway [6] or the different classes of tactile neurons in the somatosensory periphery [7]. Theoretical studies have shown how response properties and class membership of individual neurons can be optimized to maximize joint information coding in the population [810]. However, because this optimization relies on the full population, predicting how or to what extent an individual neuron contributes to population coding becomes impossible without considering the properties of other neurons that make up the population. Given these findings, it is thus paramount to study the population activity of sensory neurons in order to understand what stimulus information is available at subsequent processing stages.

Tactile interactions are mediated by mechanoreceptive afferents and the glabrous skin of the human hand is innervated by approximately 17,000 fibers [11]. These are divided into different classes based on their response properties and receptive fields. Three classes are mainly involved in discriminative touch: slowly adapting type 1 afferents (SA1) exhibit small receptive fields and respond to static or low-frequency indentations, rapidly adapting afferents (RA) possess slightly larger receptive fields and respond to dynamic flutter stimuli, and Pacinian afferents (PC) exhibit extremely large receptive fields and are most responsive to high frequency vibrations. These classes also differ in the density with which they innervate the skin, both compared to each other and at different locations on the skin [11]. A stimulus applied to a specific skin area will typically activate hundreds if not thousands of afferents of different classes all responding with distinct spiking responses [12]. However, peripheral neurophysiological measurements are subject to technical limitations, and typically only one or a small number of afferents are recorded at once. Moreover, many studies place the stimulus directly above the targeted afferent’s receptive field hotspot, in an effort to maximize neural responses within the limited recording window, but such a setup implies that responses from receptors located away from the contact location will be neglected. Given these constraints, afferent activity on a population level has scarcely been investigated, and, consequently, our understanding of how tactile information is represented in the peripheral population is limited (though see [13] for a summary of tactile population codes).

A particular source of debate in the tactile literature has been the role of different afferent classes. Traditionally, each afferent class was thought to carry information about different and complementary stimulus features [14]. However, more recently, it has become clear that most natural stimuli elicit responses from multiple afferent classes simultaneously (see summary in [15]), for example, in texture perception [16]. Furthermore, both experimental evidence [17] and computational modeling [18] suggest that information from multiple classes of afferents is integrated in cortex, if not before, and psychophysical studies have revealed that the quality of a tactile percept does not necessarily depend on receptor class [19]. However, to what extent peripheral tactile population activity carries complementary information about relevant stimulus features in different afferent classes has not been quantified and it is therefore unclear when and how it would be beneficial to integrate such information.

Here, we investigate the contribution of large neural populations in tactile stimulus coding and examine the interplay of tactile submodalities in this process. Because the lack of population level data currently precludes empirical study, we used a large-scale computational model, Touchsim [20], to simulate the activity of hundreds of peripheral tactile afferents of three classes in response to naturalistic stimuli, similar to those commonly used in experimental settings. First, we parametrically studied the role of afferent density in single-class afferent populations to explore if and how the composition, and particularly the number of afferents, affects the stimulus information encoding. Secondly, we considered the three classes together and asked whether each class encoded complementary or redundant information regarding stimulus features. Finally, we assessed the importance of temporal and spatial encoding precision when considering afferents on a population level. Overall, our work demonstrates that a population-level view of tactile coding is crucial for a thorough understanding of tactile information processing.


We used a large-scale neural simulator [20] to simulate the spiking activity of individual afferents belonging to three afferent classes (SA1, RA, PC) jointly spanning the range of tactile sensitivity. In our setup, we simulated the responses of a population of receptors placed along a line extending outwards from the contact location of the stimulus probe (see Fig 1A). This spatial arrangement of receptors allowed for systematic manipulation of the receptor density in the simulations. Sixteen different afferent populations with a density ranging between 1 and 140 afferents/cm2 were considered for each afferent class. Therefore, we could test the effect of low, medium, and high densities (Fig 1B) on information encoding and also directly examine natural innervation densities, such as those encountered on the palm or finger (Fig 1C). The simulated stimulus was a circular probe indented into the skin and then vibrated. We varied four stimulus features systematically across trials: the probe size (1–4 mm), the ramp amplitude (0.3–1.2 mm), the ramp length (10–50 ms), and the vibration frequency (0–200 Hz) (see Fig 1D and Methods). These parameters were chosen to span the range of tactile stimuli that are typically experienced. They are also similar to stimuli commonly employed in neurophysiological experiments, such as those used to fit the initial Touchsim model [20], and simulated responses can therefore be expected to be a close match to what would be recorded in an actual experiment. Finally, varying the stimulus across multiple parameters simultaneously ensures that the complexity of everyday tactile interactions is reproduced in the resulting population responses.

Fig 1. Simulation setup.

(A) Example of afferents terminating along a line, radiating outwards from the probe center (indicated by the arrow). The probe has circular shape (of varying size) and is centered on the origin of the line. Dots of different colors correspond to different afferent classes (separated in the illustration to facilitate visualization). (B) Example of afferent populations with different densities. (C) Representation of afferent densities measured on the human hand and corresponding simulated populations distributed over a line that mimic the densities observed in the palm and finger. (D) Illustration of the different stimulus features considered: probe size, vibration frequency, ramp length, and ramp amplitude.

To analyze the simulated responses, we coupled advanced machine learning techniques with information-theoretic analysis to compute how much information about each stimulus feature was encoded in the activity of different populations of afferents (see Methods). In brief, after simulating the spiking responses (Fig 2A), we first used Non-Negative Matrix Factorization (NMF) [21] to succinctly capture the spatiotemporal patterns of neural responses for each afferent class (Fig 2B). This technique linearly decomposes each single-trial spatiotemporal sequence of spike trains into a sum of non-negative spatiotemporal modules (describing commonly occurring population activity patterns across neurons and time) and non-negative activation coefficients (describing how strongly each pattern is recruited in a given trial). For this first stage, we chose an unsupervised technique that did not take into account the specific stimulus features used to generate the responses in each trial, because this provides an effective and relatively hypothesis-free way to describe neural responses to all possible stimuli. The specific choice of NMF was made because this technique provides a natural decomposition for spike trains, which are by nature non-negative, because it can give accurate single-trial representations of activity even when neural responses are non-orthogonal and overlapping from trial to trial, and because both its basis functions and coefficients are biologically interpretable in terms of commonly occurring activity patterns and their activation strength in each trial, respectively [22, 23]. We decided on using spatiotemporal decompositions for this stage, as used in previous studies [2426], rather than e.g., decompositions along only the spatial or temporal dimension, or tensor decompositions that assume separability in space or time [22, 23], in order to avoid introducing strong hypotheses about the spatiotemporal nature of neural population responses, which may be difficult to test or which may bias comparisons across afferent classes that may have different degrees of interdependence between spatial and temporal structure.

Fig 2. Analysis pipeline and calculation of information.

(A) Spike trains are generated using the Touchsim simulator. (B) The spike matrices are then decomposed using the Non-Negative Matrix Factorization (NMF) method, obtaining a set of non-negative activation coefficients and modules. (C) A Generalized Linear Model (GLM) fed with the neural activity captured in the NMF activation coefficients gives the probability of observing each stimulus feature. (D) Probabilities are used to compute mutual information (MI), representing the information that the neural activity carries about the stimulus.

In the second stage, we extracted specific features from the NMF representation using a supervised technique. Following previous work [27], we approximated the probabilities of occurrence of the NMF activation coefficients using a Generalized Linear Model (GLM; see Fig 2C). We then used this probabilistic model to compute the posterior probability of each stimulus feature given the observation of the spatiotemporal population spike train in each trial. Finally, we computed the information using the posterior probabilities between the presented and the decoded stimulus (Fig 2D). This procedure provides a data-robust but effective lower bound to the total information carried by population activity [2]. We also checked the robustness of our main findings using a control analysis employing a simpler decomposition model (see Methods and S2 Fig), where the NMF decomposition is only applied along the spatial axis [28], yielding a larger number of coefficients for the classifier. This analysis shifts part of the analysis from unsupervised to supervised and can therefore be expected to extract more information overall, but it makes strong assumptions about the separability of the spatial dimensions (as it decomposes only simultaneous responses across neurons), which may not be suitable for some afferent classes for which spatial and temporal response profiles may be non-separable (see below for some examples).

Information carried by individual afferent populations

In a first analysis, we investigated the information carried by each of the three afferent populations separately. To understand which afferent population best encoded any given feature, and how the information depended on the spatial density of the afferents, we calculated the total information carried by each population (Fig 3A) by simulating responses with different spatial receptor densities.

Fig 3. Effect of afferent density on stimulus feature coding.

(A) Information content (normalized by the stimulus entropy) for different stimulus features provided by single-class afferent populations of varying density. Solid lines represent the average over 40 trials, shaded regions represent standard deviation across NMF instantiations. Dotted vertical lines indicate information saturation points. (B) Saturation densities for each feature and afferent class. (C) Maximum information content provided by each afferent class at the saturation density for each feature. Error bars represent standard deviation across NMF instantiations.

For encoding stimulus size, we found that SA1 afferents were most informative, with information increasing and then saturating at a density of 40 afferents/cm2. RA afferents provided more information at very low densities and saturated at a lower level (20 afferents/cm2). In contrast, PC afferents did not carry any information about stimulus size at any of the densities considered. This result can be explained by the fact that PC afferents exhibit extremely large receptive fields [29], certainly larger than the differences in size between the stimuli we applied.

Next, we considered the encoding of the frequency of stimulation. PC afferents provided the highest frequency information, as predicted by the fact that PC afferents are well known to carry frequency information in vibrotactile stimulation [14]. Given their large receptive field size, frequency information of PC cells already saturated with the lowest density of afferents considered. In agreement with previous studies, RA afferents also carried considerable information about frequency [14]. SA1 populations carried low amounts of frequency information at small spatial densities, but slightly exceeded the frequency information of RA afferents at higher spatial densities. This result may appear to contradict earlier empirical studies, where SA1 afferents were shown to respond only to the lower extreme of the range examined in our study [30]. However, in our simulations, the sinusoidal wave is superimposed on a ramp-and-hold indentation. This sustained indentation causes low spiking activity in the SA1 afferents, with spikes aligned to the vibration (see panel B in S1 Fig). Our finding suggests that this information emerges when taking into account the activity of SA1 afferents on a population level rather than single afferents separately.

PC afferents were also the most informative class about the stimulus ramp length, followed by SA1 and RA afferents, which provided similar levels of information, but required higher densities than PCs to reach saturation. Finally, SA1 afferents carried the highest amounts of information about ramp amplitude, with PC afferents not encoding any information. RA afferents again provided higher information than SA1 ones at the lowest density, but adding more fibers did not increase information for this class.

The information saturation density, which we defined as the smallest value of density at which the population carried the asymptotic value of information reached for the highest simulated density, was the highest across classes for SA1 afferent for all considered stimulus features. Conversely, the information saturation density was the smallest for PC afferents in all cases (Fig 3B). Notably, when considering purely spatial features such as the stimulus size, the population encoding the highest asymptotic information level corresponds to the one with the highest saturation density (Fig 3B and 3C). Consequently, a high density of afferents is required to extensively innervate a skin area and discriminate between fine differences in the shape of stimulation. On the other hand, when looking at temporal features such as the frequency or the ramp length, sparsely distributed PC afferents overcome the information content encoded by the other more densely packed afferent classes.

Finally, our result shows that the RA class at saturation density always encodes less information than the SA1 and PC populations about any feature considered in this study (Fig 3C). However, at low densities (<10 afferents/cm2), RA afferents were more informative than SA1 for all features considered, suggesting that the optimal way to encode a tactile feature might depend on the number of neurons available.

Information encoded by multiple afferent classes

Next, we investigated how tactile stimulus information was encoded in the joint activity of multiple afferent classes. In particular, we asked whether the information about stimulus features carried by an afferent class adds to and complements the information carried by other classes or whether the information carried by different afferent classes is redundant. To answer this question, we computed the information carried about each stimulus feature by the joint activity of populations of two or three afferent classes and compared the resulting values with the single-class information calculated above. Specifically, we used the concept of complementary information [31]: we defined the complementary information carried by additional afferent classes over that of a reference class as the information carried by all considered classes jointly subtracted by the information carried by the reference class alone. All possible combinations of classes were considered. In these calculations, unless otherwise stated, we set the density of each class to the one measured on the glabrous skin of the human finger (see Methods for details). This allowed us to compare the information contribution of different classes in a realistic and biologically relevant setting.

We first considered whether afferent classes that were not the principal source of information about a stimulus feature added information that was complementary to that of the principally contributing afferent class. To do so, for each feature, we quantified the amount of complementary information that the less informative classes add to the information carried by the most informative class (Fig 4A). The amount of this complementary information was normalized to the amount of stimulus information carried by the most informative class. For all features, we found that the second and third most informative classes added information that complements the information carried by the most informative class alone. On average, the second most informative class added between 12 and 25% complementary information, depending on the feature considered. When considered jointly, the second and third most informative classes added, on average, between 15 and 30% of complementary information, compared to the most informative class alone. This result indicates that for each tactile stimulus feature, each class encodes some amount of complementary information about the stimulus that cannot be found in the activity of the other two classes.

Fig 4. Integrating information across afferent classes.

(A) Information gain considering the two and the three most informative classes together with respect to the most informative class alone. The density measured on the human finger was taken for each afferent class. Thin lines correspond to different instantiations of the NMF decomposition, and the thick dashed lines correspond to their averages. (B) Decomposition of information into redundant and complementary contributions of each class with respect to the remaining two classes together. Information for each afferent class has been normalized to 100% and was calculated at the density measured on the human finger. Note that in both panels (A) and (B) only two afferent classes were considered for the analyses regarding stimulus size and ramp amplitude since the third class (PC) was carrying null information (see Fig 3C).

Next, we investigated which amount of each afferent class’s information contribution was complementary or redundant when considered against the information contribution of the other afferent classes. For each stimulus feature and each individual afferent type, we computed the fraction of the information carried by the considered afferent that is complementary with respect to the information already carried by the other two afferent classes. This fraction is an index of the specific novelty of the information of a given afferent class with respect to all others (Fig 4B). In general, a significant fraction of information carried by each afferent class was complementary to that of other classes. In most cases, however, this fraction was not close to 1, meaning that there was also redundancy between the information carried by afferent classes. When examining how this fraction varied across stimulus features, interesting patterns emerged.

For vibration frequency and ramp length, the two stimulus features for which all three afferent classes encoded considerable information, we found mostly redundant coding, with relatively small fractions of complementary information (on average 13% for frequency and 23% for ramp length, Fig 4B). All three classes encode vibratory stimuli by locking their spiking activity to the sinusoidal traces, which explains the redundancy across classes. However, the fact that the frequency ranges encoded by each class do not completely overlap explains the existence of significant fractions of complementary information across all three afferent classes. Given that all three classes encoded large amounts of frequency information, the actual amount of complementary information added by each class was surprisingly large (Fig 4B). A similar pattern of complementarity and redundancy of information was observed for ramp length, which like frequency is a dynamic feature that depends on timing.

For stimulus size, the SA1 afferent population carried most of the information (Fig 3), and this information had a high value of complementarity (72%), indicating that it could not be found in other afferent types (Fig 4B). The RA afferent population added less information (Fig 3), but also exhibited a relatively large fraction of complementarity (48%) (Fig 4B). The encoding of size for SA1 and RA afferents seems to depend on the number of afferents that are activated by the stimulus (Fig A), and the observed complementarity between RA and SA1 afferents is partly due to differences in spatial sensitivity across the two populations. Information carried by PCs about probe size was negligible and therefore this class was not considered in the complementarity analysis for this feature.

Finally, for ramp amplitude we found results that resemble those for stimulus size. The SA1 population carried most information, which was largely complementary (63%) to that of other classes. RA afferents carried less information than SA1 afferents, but part of this information (43%) was complementary to that of SA1 afferents. In this case, the encoding appears again to depend on the fraction of afferents that are activated by the stimulus, as was the case for stimulus size. This is a genuine form of population coding that would not be evident from single afferent analyses. PCs again provided negligible information (see Fig 3C), and thus were not taken into account.

Effect of afferent density on complementary information

Having established that information about individual stimulus features is carried by multiple, rather than single, afferent classes and that different afferent classes often carry complementary stimulus information, we next asked how the complementarity of information depends on the spatial density of afferents. We were especially interested in whether, given the functional properties of afferents in each class, it would be more efficient to allocate all receptors to the most informative class or spread the receptors across different classes to take advantage of the complementarity of different classes. To address these questions, we systematically analyzed the information carried by individual afferent classes and their combination at different densities (Fig 5A). We tested the same upper and lower density limits as used previously. For a more realistic comparison with human biology, we also considered two other cases of spatial density arrangements, in which each population has a density equal to that experimentally found either in the palm or in the finger of the human hand (see Methods for precise numbers).

Fig 5. Information gain at different afferent densities.

(A) Radar plot of the information content provided by single and combined classes at different densities for individual tactile features. Each radial axis represents the information content of a single afferent class or combination of two classes. Dotted circular lines correspond to the information given by the three afferent classes together. Information is normalized for each stimulus feature with respect to the information provided by the three classes altogether at the upper-limit density (i). Four different density sets were considered: (i) lower limit, (ii) human palm, (iii) human finger, and (iv) upper limit as reported in the Methods. (B) Comparison of the information gained when doubling the density of the most informative class (central bar) or when combining with a different population (right bar). The baseline density (left bar) was set at 10 afferents/cm2. Error bars represent standard deviation across different NMF instantiations.

A substantial increase in the amount of encoded information was found for all features when increasing the density from the lower limit to realistic densities. Conversely, increasing the densities from the finger values further to the upper limit did not lead to additional increases of encoded information, neither when considering individual classes nor their combination, suggesting that the information in multi-class population coding saturates similarly to single-class coding. The only exception was probe size, for which information content at the upper limit was higher than at the finger density. As shown in Fig 5B and summarized in Table 1, stimulus size is also the only feature for which increasing the density of the most informative class, SA1, improves the information content more than combining different classes. As discussed previously, stimulus size is a purely spatial feature, and a high density of afferents is necessary to discern small differences in the shape of the stimulus. In contrast, for all other features considered, combining the content of the two most informative afferent classes yields more information than doubling the afferent density of the most informative class alone.

Table 1. Information maximizing encoding strategies for each stimulus feature trading off increases in innervation density for a single afferent class versus adding fibers from a different class.

Together, these results show the advantages for information encoding at the population level of spreading information across classes of receptors with complementary information rather than simply packing more receptors of a given class into the skin, even if receptors of this class are highly informative about the stimulus.

Contributions of the spatial and temporal organization of population activity to population coding

After establishing how much information is encoded by each afferent class and their combinations, we investigated the nature of the population coding in more detail. In particular, we asked two questions relevant to understanding the spatial and temporal organization of the population code. First, how important is the precise temporal structure of the population activity for decoding stimuli from spatiotemporal patterns of neural population activity? Second, how important are differences in spatial neuron-to-neuron response profiles to decode stimulus information from spatiotemporal population activity?

The importance of the spatial structure of the afferent population code for information coding, that is, the afferent-to-afferent difference in stimulus tuning properties at different spatial locations, is supported by the finding that natural tactile stimuli elicit specific firing patterns in afferents located in different places [13, 32]. A critical role for the temporal structure of individual afferent activity has been demonstrated in previous studies [16, 33, 34] and is also supported by the fact that thalamic and cortical somatosensory neurons also encode tactile information with millisecond-scale spike timing precision [3538]. However, it is unknown whether these expectations would hold at the level of afferent population coding. For example, precise spike timing might be less important when considering a full population of afferents rather than a single one. Furthermore, information in the spatial and the temporal structure might be redundant, such that for example information contributed by the spike timing of the population may be redundant with the information encoded in the spatial structure, or vice versa. Addressing these questions, therefore, requires a direct test with a large population.

First, we evaluated whether the distribution of afferents in space, parameterized in the simulation as the distance of the afferent location from the stimulation site, impacts the population coding capabilities. To do so, we kept the NMF spatiotemporal modules computed on the original data and recomputed the NMF activation coefficients on the spiking activity obtained after destroying spatial information by randomly shuffling the neural responses across neurons for all time bins. Then we used the classifier trained on the original data to compute how much information about each stimulus feature could be decoded (see Methods). We called this “space-coding removed” information. The difference between the original and the space-coding removed information quantifies how much of the information in the original, unshuffled spike trains can only be expressed and decoded because of the spatial structure of the code. Note that this quantification is performed at a fixed spatial density, and it is thus different from the previous analyses of the effect of changing the spatial density. We found that, after destroying the spatial structure, information content dropped, averaged across classes and features, by 19% (Fig 6A). The loss of information was higher for SA1s (30%) compared with RAs (5%) or PCs (25%). Thus, the nature of how information is distributed across neurons at different locations provides a contribution to population coding that is lost and cannot be recovered by the temporal organization of activity when the spatial structure is destroyed.

Fig 6. Information recovered after destroying spatial or temporal coding.

(A) Information recovered for each afferent class after neglecting afferents’ spatial organization normalized with respect to the maximum information content in the original data at the saturation level (see Fig 3C). (B) Information recovered for each afferent class after destroying precise spike timing at different timescales. Again, information is normalized with respect to the maximum information content in the original data at the saturation level. Error bars represent standard deviation across different NMF instantiations. Note that for both stimulus size and ramp amplitude, information carried by PC class was null (see Fig 3C) and such class was excluded from these analyses.

Next, to quantify the specific contribution of temporal structure to decoding information from the spatiotemporal population activity, we computed a “time-coding removed” information value from the population responses. To do so, we randomly shifted the spikes with each shift independently drawn from a uniform distribution with range ±2, ±5, and ±10 ms before recomputing the NMF activation coefficients and estimating the information content (see Methods). The difference between the original and the time-coding removed information quantifies how much of the information that was decoded from the spatiotemporal population activity is contributed by the millisecond-scale temporal structure of the code. We found that, after destroying the temporal structure of the data with time shifts, the information decoded from spatiotemporal neural activity dropped, averaged across classes and features, by 7% with the 2 ms shift, 19% with the 5ms shift, and 28% with the 10 ms shift (Fig 6B). Information loss was highest for PCs (24%, 41%, and 61%, respectively, for the different time shifts) and relatively lower for RAs (5%, 15%, and 20%) and SA1s (2%, 12%, and 20%). Notably, across our set of stimuli, we found higher information loss when neglecting the temporal resolution of spike trains rather than the spatial distribution. This result indicates that even in large afferent populations spike timing with high temporal precision remains an important part of the spatiotemporal neural code. In particular, when examining features that can be expected to mostly rely on temporal structure, such as vibration frequency, information dropped significantly already with the smallest jitter of 2 ms. In contrast, other features, such as stimulus size, appear to rely more on spatial than temporal activation. For these features, information content was preserved when disrupting the temporal code but decreased abruptly when destroying the spatial structure in the data. Finally, we noticed that for some stimulus features the information content increased slightly after destroying spatial or temporal structure. These small increases should not be interpreted as indicating that more information is available at lower temporal resolutions (which would contradict the Data Processing Inequality), but rather indicating that in such cases we could not find evidence of timing information contributing at a finer scale. These effects arise spuriously in any decoding method that computes a lower bound on the population information, whose tightness may vary to some extent across conditions. They might also reflect how we simulated noise: since we simulated motor noise by varying parameters such as indentation depth, most of the neural noise in the afferent responses is likely correlated; adding a small, uncorrelated temporal jitter might have helped with decoding in some cases.


This study is based on a simulation paradigm, which provides novel insights on stimulus coding by tactile afferent populations. Much of our current understanding of encoding mechanisms of tactile stimuli derives from electrophysiological studies. However, these are severely limited in the number of afferents that can be recorded at a time. In addition, many previous studies have focused only on those afferents terminating directly at the stimulus contact location. Thus, a biased picture of tactile coding might have emerged. In fact, to our knowledge, population coding of tactile afferents, taken as the spatiotemporal activation of multiple afferents belonging to one or more classes, has scarcely been investigated before. Here, we used a recently developed computational model that allows simulation of tactile neural responses at the population level with high accuracy. Although any putative population-level coding mechanisms derived from modeling would need to be experimentally verified, this approach allows investigating aspects of the neural code that are currently experimentally intractable and can therefore generate ideas for potential downstream decoding mechanisms.

Single-class coding and receptor density

We first investigated how the density of afferents from a single class plays a role in the encoding process. We showed that the information content of both SA1 and RA populations increases asymptotically with afferent density until saturation. This effect was consistent for all features considered, although the specific saturation densities varied between features. This result highlights that tactile information is generally spread across a population of multiple afferents, even for features that are not explicitly spatial. Furthermore, the afferent class most informative about a tactile feature at low innervation densities might be different from the most informative class at high densities. Consequently, judging or predicting the information content of a population from recordings of single afferents only might be misleading and provide a biased picture of how information is represented in full populations.

In contrast to SA1 and RA afferents, the information level for PC afferents was essentially constant for all density values considered across all tactile features. While this result might be taken to suggest that the PC population does not contribute information above that of a single afferent, there is evidence to suggest that PC populations might be important in different tactile contexts than the ones explored here: making contact with surfaces causes mechanical waves to spread throughout the hand, activating PC afferents as far away as the palm and their joint population activity carries information about how contact is made and other aspects of the grasp [39].

It should be noted that for all afferent classes, the minimum density needed to recover the maximum information for any tactile feature is lower than the empirical afferent densities estimated for the human hand [11]. We speculate that the minimum density of afferents required to reach the information saturation might be higher for more complex features. Indeed, as an initial investigation into the power of large-scale neural simulations on a population level, this study considered relatively simple stimulus features compared to the complexity of realistic tactile interaction. Similarly, previous studies showed a strong relationship between SA1 density and tactile spatial acuity [11]: afferents, particularly of SA1 type, need to be densely packed in the skin to resolve and discriminate extremely fine features. While our setup included one clearly spatial stimulus (probe size), none of the others were purely spatial. Finally, afferent innervation densities across most of the skin of the human body are much lower than those in the hand and indeed within the range identified in the current study, suggesting that our stimulus set was covering a large part of the physiologically relevant range.

Interestingly, we found that the RA class at saturation density tended to encode less information than the SA1 and PC populations, but in contrast, was more informative than SA1s at low densities (<10 afferents/cm2). This result suggests that the way information is spread across afferent classes depends in part on receptor density, and in turn, should affect optimal decoding downstream. Indeed, tactile innervation density changes dramatically across different body areas, both in terms of the absolute number of afferents and relative innervation densities of different classes [11], and it is possible that changes in the class composition at different skin sites partially reflect density-dependent optimal allocation of afferent classes. Our findings also suggest that tactile information need not be linked firmly to a given receptor type, but that information is spread in a dynamic way across different afferent classes (see [19] for a concrete example in frequency coding).

Complementarity and redundancy across afferent classes

The second step of our analysis was to consider combinations of afferent classes and to evaluate their information content with respect to different stimulus features. Here, we found both redundant and complementary contributions to the information across afferent classes. All afferent classes generally provided at least some complementary information about stimulus features, suggesting that downstream areas should integrate information from different classes to maximize information (see also [15]). Quantifying such complementary information is a necessary first step towards further study of submodality convergence in the stimulus encoding process, especially considering that directly accessing the integration mechanisms in humans is complicated. Convergence has previously been inferred from cortical recordings in primates for multiple individual stimulus attributes [18, 40, 41], but here we quantitatively demonstrate that information is spread across afferent types in most cases, and therefore, submodality integration can be expected to be a general feature of downstream processing.

Not all information was complementary however, and we also found considerable degrees of redundancy between afferent classes. Redundancy in neural coding has been extensively debated (see [42] for a review) and can be a strategy for robust stimulus encoding. Indeed, over-representing stimulus information using large populations of neurons increases the probability of having a relevant impact in downstream neurons, guaranteeing—or, at least, making more plausible—that critical information is processed while negligible information is discarded—or less likely used—. Redundancy can rank information according to relevance, overcoming the associated coding inefficiency in favor of a significant performance increase [43]. Furthermore, redundancy could be interpreted as a strategy to make the neural code resilient in the event of temporary or permanent lack, shortage, or failure of input from an afferent class. This theory is supported by recent findings in an experimental study in mice that showed that the use of genetic ablation strategies to suppress the response of either rapidly or slowly adapting afferents leaves responses in the somatosensory cortex mostly unchanged [17], which implies that the required information can be recovered from the remaining afferent input. This would not have been possible if the two classes had encoded complementary information only. Such a process might be beneficial when several features are processed simultaneously, and redundancy between classes might help to disambiguate the stimulus.

Information maximizing receptor selection

We investigated whether increasing the density of afferents of a given class or combining them with afferents of a different class yields higher information gain. We found that adding afferents belonging to a different class was generally more efficient than increasing the density of the most informative class by the same amount, confirming that the information about stimulus features is not segregated in single afferent classes, but is spread across them. Indeed, while absolute tactile innervation densities vary widely across the body, the fraction of slowly adapting afferents at any given site varies only between 40 and 70% and is relatively evenly split for most body regions [11], especially for those with lower innervation. Our results suggest that such a composition increases information transmission, while minimizing fiber count. The number of tactile fibers that can fit into the nerves and spinal cord is naturally constrained, and consequently, extensive skin areas are innervated at low density. Neurons are also energetically expensive, and therefore it is plausible that evolutionary optimization might have maximized the ratio between information and energy consumption by spatially distributing the mechanoreceptors and diversifying response properties across different receptor classes.

In several sensory systems other than touch neural populations are also composed of multiple cell classes with distinct response properties. Indeed, early sensory pathways frequently split into different classes with disparate response properties (e.g., the large number of retinal ganglion cell classes [6]). According to the efficient coding hypothesis, sensory systems have evolved to optimally transmit information about the surrounding world, given constraints on their biophysical components and energy use [44]. This theory also explains splitting a population into two or more cell classes as a strategy to maximize information transmission, as shown in previous studies for different sensory systems [810]. Our findings support this hypothesis, showing that, in most cases, a combination of classes was more informative than a single class higher-density population.

Limitations and future work

Our study focused on the three main classes of tactile afferents that mediate discriminative touch. However, other classes also contribute to tactile coding, such as SA2 afferents, which are thought to primarily signal skin stretch but are consciously perceivable [45]. Furthermore, tactile innervation and neural response properties differ somewhat in the hairy skin [11], which covers most of the human body. Thus, our results will most directly reflect tactile coding on the human hand, but future studies should consider how these results might extend to other regions of the body.

As the findings are based on computer simulations, the veracity of the results will depend on the accuracy with which the spiking responses can be replicated in the computational model. The stimuli we used, namely indentations by a single probe orthogonal to the skin surface with a superimposed vibration, are similar to those on which the original model was fit and fall into the range where it has been validated most extensively [20]. Still, by combining multiple tactile features, we believe that our simulated stimuli are sufficiently complex, varied, and natural that the resulting findings can be considered of behavioral importance. One avenue for future research would be to investigate information transfer on tactile inputs arising from natural behaviors such as grasping and manipulating objects, which include multiple contacts, shear forces, and movement between the object and the skin. However, this would require further work on the precise spatiotemporal force patterns on the hand during such behaviors and spiking models that take into account more complex afferent response properties (see [46] for an example).

To study the effects of different innervation densities, we considered a simplified setup, distributing the afferents over a single dimension while neglecting some properties affecting the spatial distribution of afferents, for example the complex shape of the human hand. Future studies should take this aspect into account to reveal how the shape of the hand, the different afferent densities, and the composition of the population in different areas of the hand plays a role in stimulus encoding. In the same direction, population coding strategies and afferent distribution might be coupled with natural stimulus statistics in different body areas to deepen the understanding of how the human somatosensory system is optimized to receive and process natural tactile stimuli.

Finally, as in other information-theoretic analyses on large-dimensional neural response spaces, our analysis can only provide a lower bound on the true information contained in the population spiking patterns. As direct calculation of the information is prohibitive with respect to the amount of data that would be required, we chose a method that decomposes the high-dimensional responses, with information values subsequently estimated in the lower-dimensional space. An additional benefit of this method is that the initial unsupervised decomposition of the neural responses reflects aspects of neural processing in sensory pathways. However, the resulting information values will be affected by choices regarding this decomposition specifically, and the information calculation more generally, and different choices might yield somewhat different outcomes. To directly test the robustness of our method, we compared our method with a different analysis pipeline. We found qualitatively very similar results, suggesting that our main findings, for example regarding the benefits of integrating across different afferent classes, hold generally, rather than being dependent on the specific analysis method chosen.


Simulation of spiking activity

To generate the spiking activity of tactile afferents, we used a previously published and validated model called Touchsim [20]. We employed the model to simulate populations of SA1, RA, and PC afferents terminating along a line of 1 cm for SA1s and RAs, and 5 cm for PCs radiating outwards from the stimulus location. The density of afferents varied between 1 and 140 afferents/cm2 for a total of 16 different populations per afferent type. This range includes the physiological innervation densities estimated for the human hand [29]. In some analyses, we also directly set individual class densities to those of the human palm or finger (see Table 2 for precise values).

Table 2. Estimated innervation densities of afferent classes (afferents/cm2) for different regions of the human hand [29].

We designed stimuli with circular shapes, which are indented in the skin following a ramp-and-hold function (see Fig 1 B). When the maximum amplitude of the ramp is reached, a sinusoidal wave is superimposed. This setup simulates well-established psychophysical setups in which a probe is brought into contact with the skin and then vibrated at a set frequency. It also includes many aspects of natural tactile stimulation: indentation, retraction, and constant stimulation at different depths and spatial scales, as well as vibrations at different frequencies. Individual stimuli are created by varying 4 different features: 1) the stimulus size (4 conditions: [1:1:4] mm), 2) the maximum ramp amplitude (4 conditions: [0.3:0.3:1.2] mm), 3) the ramp-up time (5 conditions: [0.01:0.01:0.05] s), and 4) the frequency of the superimposed sinusoidal wave (10 conditions: [0, 10, 20, 40, 60, 80, 100, 130, 160, 200] Hz). This setup yielded 800 unique stimuli, and the afferent response to each was simulated for 40 trials. The model included simulated neural noise. Additionally, in order to simulate environmental noise such as motor noise during active touch, we jittered the stimulus location (by ± 0.3 mm), the amplitude of the sinusoidal wave (by ± 0.05 mm), and the ramp amplitude (by ± 0.1 mm) on every trial.

Unsupervised spatiotemporal NMF

Information calculations from high dimensional data require prohibitively large datasets. A common strategy to address this issue is by first performing unsupervised dimensionality reduction on the data. Here, we used spatiotemporal Non-Negative Matrix Factorization (NMF) to decompose the spatiotemporal matrix of spiking responses across the population.

Responses were discretized by binning the spike trains into 2-ms intervals and counting the number of spikes falling into each bin. This resulted in a matrix RRM × TN, where M is the total number of trials, T the number of time bins, and N the number of afferents in the population. NMF decompositions are naturally suited to describe spatiotemporal matrices of spiking responses, because spike trains are non-negative, and because commonly occurring spike patterns may be non-orthogonal and partly overlapping, and NMF does not require assumptions of orthogonality or non-overlap of different activity modules. NMF describes a single trial spike train as a sum of trial-independent non-negative spatiotemporal modules (describing the most often recurring spatiotemporal firing patterns) and trial-dependent non-negative activation coefficients representing the strength of recruitment of each module in the considered trial [22, 23]: (1) where HRM × K contains the non-negative activation coefficients for the K modules in each trial and WRK × TN contains the non-negative modules. We used the function NMF included in the scikit-learn Python library [47] to calculate the NMF decomposition.

We performed the NMF decomposition separately for each of the three afferent classes at each density value considered. Beforehand, we randomly separated the whole set of trials into balanced sets with a 25/75 split. We used the 25-set to determine the number of modules K as the minimum number of modules capable of explaining a selected level of variance of the original data in R, as follows. First, to consistently select the level of variance explained between populations of the same class but with different densities, we calculated the saturation level of the accounted variance for each population considered (tolerance <1%). We averaged the saturation levels across populations of the same class with different densities and used this value as the new threshold for the explained variance. Finally, we calculated k modules W and activation coefficients H on the same 25-set. Given the W modules from the 25-set, we computed the activation coefficients H on the remaining 75-set. Given the random initialization of the spatiotemporal basis functions with the NMF decomposition, we computed 50 instantiations of the NMF to account for the variability of the method.

Stimulus decoder

After dimensionality reduction, we fed the activation coefficients H computed with the NMF to a stimulus decoder. We used multinomial logistic regression to decode each stimulus feature separately on a trial-by-trial basis based on the neural activity (similarly to [27]). The scikit-learn Python library [47] was used for the implementation. This type of classifier uses a linear function f(s, i) to predict the probability of outcome s for trial i such that: (2) where Hi is a vector containing the NMF activation coefficients for trial i and βk stores the coefficients associated with outcome s. When generalizing to Sn features, the multinomial logistic regression model consists of Sn − 1 independent logistic regression models regressed against the remaining Sn outcomes. Note that outcomes correspond to the possible values that the stimulus features could take and vary for each feature.

The 75-set was divided equally and stratified into training and test sets. We trained the classifier on the activation coefficients of the training set and evaluated performance using the activation coefficients of the test set. The training procedure was performed using a stratified 5-fold cross-validation. This process was repeated for each population of afferents (both for single and combined classes) and all afferent densities. The solver used for the fitting procedure was lbfgs in combination with L2 regularization. We selected the parameter C for the regularization by performing grid search. The scoring of the classifier was the negative log-likelihood, also known as the cross-entropy loss.

The final fitted model outputs the posterior probability of observing each stimulus feature given the neural activity captured in the NMF activation coefficients [27]. From this posterior probability, we decoded the stimulus that was most likely given the observed afferent activity.

Mutual information

Next, we computed the mutual information [48] from the confusion matrix of the decoder as follows [2]: (3) where S, stand for the set of all possible presented and decoded stimuli, respectively. denotes joint probability distribution, which is derived from the confusion matrix obtained empirically across all trials, of presenting stimulus s and decoding stimulus in a given trial. p(s) and correspond to the marginal probabilities of s and , respectively. The information in the confusion matrix is a data-robust lower bound to the total information carried by population activity. This approximation is tight when neural activity can be categorically binned into as many values as the number of distinct stimuli without losing considerable information. The information in the confusion matrix captures aspects of information processing, such as the distribution of decoding errors, which are not captured by simple measures such as the fraction of correctly decoded stimuli [2]. Since the information upper bound is the entropy of the stimulus set (indicating perfect single-trial stimulus discrimination), we normalized information values by dividing them by the entropy of the stimulus set: (4) where Sn is the number of values that the stimulus can take.

Computation of complementary information

To assess the complementarity of stimulus information carried by different classes, we computed the information carried about each stimulus feature by the joint activity of populations of two or three afferent classes and compared it to the information carried by a single-class population. We defined the amount of information carried by the pair of afferent classes that is complementary to that of a reference class as the difference between the information carried by all the classes (including the reference class and the additional ones) and the information carried by the reference class. We repeated this process, taking each class as the reference class in turn. As an example, for SA1 afferents as the reference class, the complementary information is computed as: (5)

We defined the redundant information between the additional classes and the reference class as the sum of the information carried individually by the reference class and the additional ones minus the information carried by all the classes together, such that (again, taking SA1s as the reference class): (6)

The sum of redundant (Eq 6) and complementary (Eq 5) information for a class equals the total information carried by that class.

Contribution of spatial and temporal structure of neural activity to population coding

We assessed the contribution of fine temporal and spatial resolution within the spiking activity to information coding by destroying the temporal and spatial structures in the data. To destroy the spatial structure, we randomly permuted the order of afferents in the spiking matrix R. Then, keeping the non-negative modules W obtained with the NMF decomposition on the original, non-shuffled data, we computed the activation coefficients on the spatial-shuffled data. We finally used these activation coefficients to feed the classifier previously trained on the original activation coefficients. To destroy the temporal structure in the data, we randomly shifted the spikes with a uniform distribution of ±2, ±5, and ±10 ms. Then, as for the spatial case, we obtained the activation coefficients on the time-shuffled data and used those to feed the classifier trained on the original data and estimate the residual information content after disrupting the data temporal structure.

Control analysis using spatial NMF and supervised decoding of temporal structure

To test the robustness of the analysis pipeline with respect to the choice of low-dimensional representation, we calculated information values from an alternative NMF decomposition of the data. We first computed the spike count over the whole trial, Rcount. We then applied the NMF decomposition on Rcount to find spatial modules Wspace capturing which afferents are firing together, such that: (7) The tolerance criterion for selecting the number of modules M was the same as described above for the spatiotemporal NMF.

We computed a single set of spatial modules using the population activity pooled over all time bins. We then calculated a different activation coefficient Ht for each spatial module and time bin producing in total a number of activation coefficients per trial equal to the product of the number of modules M times the number of time bins T, a larger number than the activation coefficients per trial of the spatiotemporal NMF decomposition described above (which only produced M activation coefficients per trial).

For the supervised decoding of the activation coefficients in each trial, we used the same GLM decoder described above.

Supporting information

S1 Fig. Illustrative examples of simulated spiking activity.

Responses are shown for the three afferent classes as a function of (A) stimulus size, (B) frequency, (C) ramp length, and (D) ramp amplitude. Note that we have conditioned on the remaining features for each panel and that the afferent densities chosen in this example correspond to the ones in the finger.


S2 Fig. Robustness of findings using a spatial NMF decomposition of neural responses.

The analysis presented in Fig 5A (panel iv) was redone using a spatial NMF (rather than the spatiotemporal version) to test the robustness of the results. While this analysis extracts somewhat different information values, the main result that afferent classes working together increase the overall information content is preserved.



  1. 1. Pouget A, Dayan P, Zemel R. Information processing with population codes. Nat Rev Neurosci. 2000;1(2):125–132. pmid:11252775
  2. 2. Quian Quiroga R, Panzeri S. Extracting information from neuronal populations: information theory and decoding approaches. Nat Rev Neurosci. 2009;10(3):173–185. pmid:19229240
  3. 3. Georgopoulos AP, Schwartz AB, Kettner RE. Neuronal population coding of movement direction. Science. 1986;233(4771):1416–1419. pmid:3749885
  4. 4. Schneidman E, Puchalla JL, Segev R, Harris RA, Bialek W, Berry MJ. Synergy from Silence in a Combinatorial Neural Code. Journal of Neuroscience. 2011;31(44):15732–15741. pmid:22049416
  5. 5. Adibi M, McDonald JS, Clifford CWG, Arabzadeh E. Population Decoding in Rat Barrel Cortex: Optimizing the Linear Readout of Correlated Population Responses. PLoS Comput Biol. 2014;10(1):e1003415–e1003415. pmid:24391487
  6. 6. Baden T, Berens P, Franke K, Román Rosón M, Bethge M, Euler T. The functional diversity of retinal ganglion cells in the mouse. Nature. 2016;529(7586):345–350. pmid:26735013
  7. 7. Abraira VE, Ginty DD. The Sensory Neurons of Touch. Neuron. 2013;79(4):618–639. pmid:23972592
  8. 8. Gjorgjieva J, Sompolinsky H, Meister M. Benefits of Pathway Splitting in Sensory Coding. J Neurosci. 2014;34(36):12127–12144. pmid:25186757
  9. 9. Kastner DB, Baccus Sa, Sharpee TO. Critical and maximally informative encoding between neural populations in the retina. Proceedings of the National Academy of Sciences. 2015;112(8):2533–2538. pmid:25675497
  10. 10. Gjorgjieva J, Meister M, Sompolinsky H. Functional diversity among sensory neurons from efficient coding principles. PLoS Comput Biol. 2019;15(11):e1007476. pmid:31725714
  11. 11. Corniani G, Saal HP. Tactile innervation densities across the whole body. J Neurophysiol. 2020;124(4):1229–1240. pmid:32965159
  12. 12. Johansson RS, Vallbo AB. Tactile sensibility in the human hand: relative and absolute densities of four types of mechanoreceptive units in glabrous skin. J Physiol (Lond). 1979;286:283–300. pmid:439026
  13. 13. Goodwin AW, Wheat HE. Sensory signals in neural populations underlying tactile perception and manipulation. Annu Rev Neurosci. 2004;27:53–77. pmid:15217326
  14. 14. Johnson KO. The roles and functions of cutaneous mechanoreceptors. Curr Opin Neurobiol. 2001;11(4):455–461. pmid:11502392
  15. 15. Saal HP, Bensmaia SJ. Touch is a team effort: interplay of submodalities in cutaneous sensibility. Trends Neurosci. 2014;37(12):689–697. pmid:25257208
  16. 16. Weber AI, Saal HP, Lieber JD, Cheng JW, Manfredi LR, Dammann JF 3rd, et al. Spatial and temporal codes mediate the tactile perception of natural textures. Proc Natl Acad Sci U S A. 2013;110(42):17107–17112. pmid:24082087
  17. 17. Emanuel AJ, Lehnert BP, Panzeri S, Harvey CD, Ginty DD. Cortical responses to touch reflect subcortical integration of LTMR signals. Nature. 2021;600(7890):680–685. pmid:34789880
  18. 18. Saal HP, Harvey MA, Bensmaia SJ. Rate and timing of cortical responses driven by separate sensory channels. Elife. 2015;4(12):7250–7257. pmid:26650354
  19. 19. Birznieks I, McIntyre S, Nilsson HM, Nagi SS, Macefield VG, Mahns DA, et al. Tactile sensory channels over-ruled by frequency decoding system that utilizes spike pattern regardless of receptor type. Elife. 2019;8. pmid:31383258
  20. 20. Saal HP, Delhaye BP, Rayhaun BC, Bensmaia SJ. Simulating tactile signals from the whole hand with millisecond precision. Proceedings of the National Academy of Sciences. 2017;114(28):201704856–201704856. pmid:28652360
  21. 21. Lee DD, Seung HS. Learning the parts of objects by non-negative matrix factorization. Nature. 1999;401(6755):788–791. pmid:10548103
  22. 22. Onken A, Liu JK, Karunasekara PPCR, Delis I, Gollisch T, Panzeri S. Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains. PLoS Comput Biol. 2016;12(11):e1005189. pmid:27814363
  23. 23. Williams PL, Beer RD. Nonnegative decomposition of multivariate information. arXiv preprint arXiv:10042515. 2010;.
  24. 24. Overduin SA, d’Avella A, Roh J, Carmena JM, Bizzi E. Representation of Muscle Synergies in the Primate Brain. Journal of Neuroscience. 2015;35(37):12615–12624. pmid:26377453
  25. 25. Kim SP, Rao YN, Erdogmus D, Sanchez JC, Nicolelis MAL, Principe JC. Determining Patterns in Neural Activity for Reaching Movements Using Nonnegative Matrix Factorization. EURASIP Journal on Advances in Signal Processing. 2005;2005(19):829802.
  26. 26. Wei J, Bai W, Liu T, Tian X. Functional connectivity changes during a working memory task in rat via NMF analysis. Frontiers in Behavioral Neuroscience. 2015;9. pmid:25688192
  27. 27. Runyan CA, Piasini E, Panzeri S, Harvey CD. Distinct timescales of population coding across cortex. Nature. 2017;548(7665):92–96. pmid:28723889
  28. 28. Noei S, Zouridis IS, Logothetis NK, Panzeri S, Totah NK. Distinct ensembles in the noradrenergic locus coeruleus are associated with diverse cortical states. Proceedings of the National Academy of Sciences. 2022;119(18):e2116507119. pmid:35486692
  29. 29. Vallbo’ AB, ohansson RS. Properties of cutaneous mechanoreceptors in the human hand related to touch sensation. Hum Neurobiol. 1984;3:3–14. pmid:6330008
  30. 30. Ribot-Ciscar E, Vedel JP, Roll JP. Vibration sensitivity of slowly and rapidly adapting cutaneous mechanoreceptors in the human foot and leg. Neurosci Lett. 1989;104(1-2):130–135. pmid:2812525
  31. 31. Panzeri S, Brunel N, Logothetis NK, Kayser C. Sensory neural codes using multiplexed temporal scales. Trends Neurosci. 2010;33(3):111–120. pmid:20045201
  32. 32. Saal HP, Vijayakumar S, Johansson RS. Information about complex fingertip parameters in individual human tactile afferent neurons. J Neurosci. 2009;29(25):8022–8031. pmid:19553442
  33. 33. Mackevicius EL, Best MD, Saal HP, Bensmaia SJ. Millisecond precision spike timing shapes tactile perception. J Neurosci. 2012;32(44):15309–15317. pmid:23115169
  34. 34. Johansson RS, Birznieks I. First spikes in ensembles of human tactile afferents code complex spatial fingertip events. Nat Neurosci. 2004;7(2):170–177. pmid:14730306
  35. 35. Long KH, Lieber JD, Bensmaia SJ. Texture is encoded in precise temporal spiking patterns in primate somatosensory cortex. bioRxiv. 2021;.
  36. 36. Panzeri S, Petersen RS, Schultz SR, Lebedev M, Diamond ME. The role of spike timing in the coding of stimulus location in rat somatosensory cortex. Neuron. 2001;29(3):769–777. pmid:11301035
  37. 37. Petersen RS, Panzeri S, Diamond ME. Population coding of stimulus location in rat somatosensory cortex. Neuron. 2001;32(3):503–514. pmid:11709160
  38. 38. Petersen RS, Panzeri S, Diamond ME. The role of individual spikes and spike patterns in population coding of stimulus location in rat somatosensory cortex. Biosystems. 2002;67(1-3):187–193. pmid:12459298
  39. 39. Shao Y, Hayward V, Visell Y. Compression of dynamic tactile information in the human hand. Science Advances. 2020;6(16):eaaz1158. pmid:32494610
  40. 40. Pei YC, Denchev PV, Hsiao SS, Craig JC, Bensmaia SJ. Convergence of submodality-specific input onto neurons in primary somatosensory cortex. J Neurophysiol. 2009;102(3):1843–1853. pmid:19535484
  41. 41. Carter AW, Chen SC, Lovell NH, Vickery RM, Morley JW. Convergence across Tactile Afferent Types in Primary and Secondary Somatosensory Cortices. PLoS One. 2014;9(9):e107617–e107617. pmid:25215534
  42. 42. Barlow H. Redundancy reduction revisited. Network. 2001;12(3):241–253. pmid:11563528
  43. 43. Puchalla JL, Schneidman E, Harris RA, Berry MJ. Redundancy in the Population Code of the Retina. Neuron. 2005;46(3):493–504. pmid:15882648
  44. 44. Barlow HB. Possible Principles Underlying the Transformations of Sensory Messages. In: Sensory Communication. The MIT Press; 2013.
  45. 45. Watkins RH, Amante M, Wasling HB, Wessberg J, Ackerley R. Slowly-adapting type II afferents contribute to conscious touch sensation in humans: evidence from single unit intraneural microstimulation. J Physiol. 2022;. pmid:35569041
  46. 46. Pruszynski JA, Johansson RS. Edge-orientation processing in first-order tactile neurons. Nat Neurosci. 2014;(August):1–7. pmid:25174006
  47. 47. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research. 2011;12:2825–2830.
  48. 48. Shannon CE. A mathematical theory of communication. The Bell system technical journal. 1948;27(3):379–423.