Precise and stable edge orientation signaling by human first-order tactile neurons

  1. Vaishnavi Sukumar
  2. Roland S Johansson
  3. J Andrew Pruszynski  Is a corresponding author
  1. Neuroscience Graduate Program, Western University, Canada
  2. Department of Integrative Medical Biology, Umeå University, Sweden
  3. Department of Physiology and Pharmacology, Western University, Canada

Abstract

Fast-adapting type 1 (FA-1) and slow-adapting type 1 (SA-1) first-order neurons in the human tactile system have distal axons that branch in the skin and form many transduction sites, yielding receptive fields with many highly sensitive zones or ‘subfields.’ We previously demonstrated that this arrangement allows FA-1 and SA-1 neurons to signal the geometric features of touched objects, specifically the orientation of raised edges scanned with the fingertips. Here, we show that such signaling operates for fine edge orientation differences (5–20°) and is stable across a broad range of scanning speeds (15–180 mm/s); that is, under conditions relevant for real-world hand use. We found that both FA-1 and SA-1 neurons weakly signal fine edge orientation differences via the intensity of their spiking responses and only when considering a single scanning speed. Both neuron types showed much stronger edge orientation signaling in the sequential structure of the evoked spike trains, and FA-1 neurons performed better than SA-1 neurons. Represented in the spatial domain, the sequential structure was strikingly invariant across scanning speeds, especially those naturally used in tactile spatial discrimination tasks. This speed invariance suggests that neurons’ responses are structured via sequential stimulation of their subfields and thus links this capacity to their terminal organization in the skin. Indeed, the spatial precision of elicited action potentials rationally matched spatial acuity of subfield arrangements, which corresponds to a spatial period similar to the dimensions of individual fingertip ridges.

Editor's evaluation

This paper will be of broad interest to anyone aiming to understand the neural basis of human touch perception. This is an important paper that provides compelling evidence for peripheral tactile encoding of orientation that reflects perceptual capabilities, by using a wide range of stimulus conditions. The results will be valuable to inform both future experiments and computational investigations into the neural representation and processing of small tactile spatial features at the edge of perceptual resolvability and on the emergence of invariant representations in touch more generally.

https://doi.org/10.7554/eLife.81476.sa0

Introduction

The distal axons of fast-adapting type 1 (FA-1) and slow-adapting type 1 (SA-1) neurons innervating the glabrous skin of the primate hand branch extensively in the skin, causing each individual neuron to innervate many spatially segregated low-threshold mechanoreceptive end organs (Cauna, 1956; Cauna, 1959; Nolano et al., 2003; Paré et al., 2002; Vallbo and Johansson, 1984). This arrangement results in spatially complex receptive fields with many highly sensitive zones (or ‘subfields’) distributed within a circular or elliptical area typically covering 5–10 fingerprint ridges (Jarocka et al., 2021; Johansson, 1978; Phillips et al., 1992; Pruszynski and Johansson, 2014). We have proposed that this arrangement constitutes a peripheral neural mechanism for geometric feature extraction (Hay and Pruszynski, 2020; Jarocka et al., 2021; Pruszynski et al., 2018; Pruszynski and Johansson, 2014; Zhao et al., 2018). Consistent with this idea, we previously demonstrated that both FA-1 and SA-1 neurons signal information about the orientation of edges moving across a neuron’s receptive field via changes in both their spiking intensity and the sequential structure of their spiking response (Pruszynski and Johansson, 2014). The intensity of a neuron’s response can be modulated by the degree of spatial coincidence between the neuron’s subfields and local skin deformation caused by the edge. The sequential structure of a neuron’s response (i.e. the gaps between bursts of spikes) can carry information about edge orientation because the orientation affects the sequence and timing of stimulated subfields as the edge passes over the neuron’s receptive field.

Here, we examine this peripheral signaling mechanism with respect to the ability of individual FA-1 and SA-1 neurons to signal fine differences in the orientation of edges (5–20°) moving across the fingertip and how stimulation speed affects this ability. In our previous study (Pruszynski and Johansson, 2014), we used edges that had large orientation differences (≥22.5°) compared to edge orientation discrimination thresholds found during object manipulation (~3°; Pruszynski et al., 2018) or psychophysical tasks (~10–25°; Bensmaia et al., 2008; Lechelt, 1992; Olczak et al., 2018; Peters et al., 2015), and we focused mainly on one movement speed (30 mm/s). However, during natural contacts with objects where the use of tactile information is critical, a wide range of speeds can occur (Callier et al., 2015; Cole and Abbs, 1988; Johansson and Westling, 1984; Olczak et al., 2018; Smith et al., 2002a; Smith et al., 2002b; Vega-Bermudez et al., 1991). This natural variation motivated us to analyze the capacity of individual neurons to signal edge orientation at different fixed speeds but also their ability to signal edge orientation across scanning speeds (i.e. in a speed-invariant manner). Examining speed invariance also allowed us to test the hypothesis that the subfield arrangement of FA-1 and SA-1 neurons gives rise to their ability to signal fine edge orientation via changes in the sequential structure of their spiking responses. Because the layout of a neuron’s subfield arrangement is fairly stable over a range of natural scanning speeds (Jarocka et al., 2021), the hypothesis predicts speed invariant signaling of edge orientation when spike trains are represented in the spatial domain, where the action potentials are represented with respect to the position of the stimulus on the skin at the time of their occurrence.

We recorded action potentials in the distal axon of single FA-1 and SA-1 neurons innervating human fingertips while tangentially scanning their receptive field with finely oriented edges (±5 and ±10° relative to the scanning direction) at speeds spanning more than one order of magnitude (15–180 mm/s). We report that FA-1 and SA-1 neurons can signal fine edge orientation differences via changes in spiking intensity (peak and mean firing rate) but that they do so much more reliably by changes in the sequential structure of the evoked spike trains. In the latter case, FA-1 neurons perform better than SA-1 neurons, but both show the best edge orientation discrimination capacity for scanning speeds that humans naturally use when moving their hands to discriminate fine spatial features. Furthermore, when spike trains are represented in the spatial domain, their orientation-dependent sequential structuring is preserved across a wide range of scanning speeds, meaning that FA-1 and SA-1 neurons can provide speed invariant information about edge orientation. Taken together, these findings further the idea that the dendritic-like branching of first-order tactile neurons’ distal axons contributes to early processing of fine geometric features in the tactile sensory ascending pathways.

Methods

Study participants and data sample

Human participants (five males and eight females) provided written informed consent in accordance with the Declaration of Helsinki. The Umeå University ethics committee approved the study (PI: Johansson). The general experimental methodology, procedure, and apparatus have been described previously (Pruszynski and Johansson, 2014).

We recorded action potentials from single first-order tactile neurons terminating in the glabrous skin of the tips of the index, long, or middle fingertips, using tungsten electrodes (Vallbo and Hagbarth, 1968). The electrodes were inserted into the median nerve at the level of the upper arm or wrist. The present study focuses on FA-1 or SA-1 neurons, classified according to previously described criteria (Vallbo and Johansson, 1984). Of the 53 neurons recorded, 30 were FA-1 and 23 were SA-1.

Stimuli

Each neuron was stimulated by lines embossed on a flat surface that moved tangentially across the receptive field along the proximal distal axis of the finger (Figure 1A). The stimulation surface was a photo-etched nylon polymer (EF 70 GB, Toyobo Company, Japan) wrapped around a rotating drum (diameter = 59 mm).

Experimental setup and raw data.

(A) Schematic of the stimulating surface, which includes five repetitions of four oriented line stimuli. The small dots above the line stimuli are used during the experiment to align the stimulating surface with an isolated neuron’s receptive field (RF). The layout of the line stimuli is such that an isolated RF is only stimulated by a single line stimulus at any given moment in time. (B) Tick marks represent action potentials from an exemplar neuron plotted relative to the position of the stimulating surface. All three rotations (#1, #2, and #3) are shown. The superimposed traces below represent the firing rate profile, defined as the reciprocal of the interval between subsequent action potentials, for each of the three rotations. This constitutes the raw data for this study. (C) Response from an exemplar neuron for the four line orientations and three stimulating speeds. The black trace represents the mean response, and the light gray traces show responses for the 15 individual trials. Tick marks below represent action potentials for each individual trial. Scale bars show time (100 ms). (D) Same data as in C but represented in spatial coordinates. Scale bars show distance traversed by the drum (2 mm). (E) Each trace shows an exemplar spike train smoothed by Gaussian kernels of various widths (noted is the SD of the kernel).

A custom-built robotic device controlled the rotation speed of the drum (see Pruszynski and Johansson, 2014). To stabilize the fingertips, the nails were glued to plastic holders firmly attached to a table that also supported the right arm and the robot, and the contact force perpendicular to the skin was servo-controlled to ~0.4 N. The instantaneous rotational position of the drum was monitored via an optical shaft encoder (AC36, Hengstler GmbH), which provided position resolution of 3 µm in the scanning direction.

Raised lines oriented –10, –5, 5, and 10° relative to the axis perpendicular to the direction of motion were presented five times on the stimulus pattern (Figure 1A). These orientations were chosen to efficiently pack the stimuli onto the stimulating surface and to permit pairwise comparison of lines with angular differences of 5 (between +5 and +10° line stimuli; between –5 and –10° line stimuli), 10 (between –5 and +5° line stimuli), 15 (between –5 and +10° line stimuli; between +5 and –10° line stimuli), and 20° (between –10 and +10° line stimuli). The lines, extending across the width of the stimulus surface (12 mm), were 0.5 mm high, and their widths were 0.5 mm at the top and 0.8 mm at the base. All lines were spaced at least 8 mm apart to minimize interactions between neighboring lines on a neuron’s response.

All neurons were scanned at eight tangential speeds: 15, 20, 30, 45, 60, 90, 120, and 180 mm/s presented in random order. A subset of neurons was scanned at up to four additional speeds: 2.5, 5, 10, and 270 mm/s. When the 270 mm/s speed was presented, it was randomly interleaved with the main eight speed conditions. When the three slowest speeds were presented, they were always done after all other speed conditions and in a set order, from fastest to slowest (i.e. 10 mm/s, 5 mm/s, and then 2.5 mm/s). These very slow speeds take a long time to complete so presenting them at the end minimized the risk of losing isolation of the current neuron during the eight main speeds and thereby creating partial datasets. All the analyses presented here focus on the main speed conditions since these were available for all neurons. Note that the same general trends hold for the additional speeds and that the raw data for all speed conditions is provided alongside this manuscript. For each neuron and speed, the drum rotated three times, resulting in 15 trials for each line stimulus and recorded speed condition (5 presentations of stimuli per drum rotation x 3 drum rotations = 15 trials).

Data processing and analysis

Sampling

The nerve signal, the instantaneous position of the stimulus surface, and the contact force were digitally sampled at 19.2 kHz, 2.4 kHz, and 0.6 kHz, respectively (SC/ZOOM, Department of Integrative Medical Biology, Umeå University).

To quantify the intensity of a neuron’s response, we used peak and mean firing rate. For each spike train evoked by an edge passing over the receptive field, we calculated peak firing rate as the reciprocal of the shortest inter-spike interval observed in the evoked response. Mean firing rate was calculated as the number of spikes evoked within an 8 mm response window (the minimum distance between adjacent elements on the drum) divided by the duration of the stimulus traversing this window. Furthermore, for each spike train, we generated the firing rate profile by computing the instantaneous frequency of the action potentials defined as the inverse of the interval between consecutive impulses for the duration of the interval. This calculated rate profile was then treated as a continuously recorded signal.

Orientation signaling as a function of scanning speed

To analyze how well a neuron signaled edge orientation at each scanning speed based on the above response measures, we used methods analogous to those previously described (Pruszynski and Johansson, 2014).

For the intensity measures, we calculated how frequently the peak or mean firing rate evoked by each presentation of a particular line orientation was closer to the average response of the remaining (14) trials with the same orientation, as opposed to the average response of 14 trials from the other three edge orientations (chance = 0.25). By including only 14 of the 15 trials from other stimuli, we avoided a bias caused by a better estimate of the average response to the other stimuli.

For a neuron’s firing rate profiles, we calculated the probability of correctly classifying an observed profile to the line stimulus that evoked it. To that end, we pairwise cross-correlated all firing rate profiles obtained at the speed in question. For each profile, we then calculated an average of the correlations between the remaining (14) trials with the same line orientation and the average for 14 trials from each of the other three line orientations. Finally, we calculated how often the highest average correlation resulted from stimuli with the same line orientation as opposed to the other orientations (chance = 0.25). We again only considered 14 other trials to avoid classification bias.

Speed-invariant orientation signaling

To investigate a neuron’s capacity to signal edge orientation in a speed-invariant manner, we applied the classification approach described above for within-speed signaling of other speed conditions. For the intensity measures, we quantified how frequently the peak or mean firing rate evoked by each presentation of a particular line orientation at one speed was closer to the average response of the trials with the same line orientation for another speed (i.e. correctly classified) as opposed to trials with the other line orientations (i.e. incorrectly classified). For firing rate profiles, we quantified how often a neuron’s firing rate profile for a given orientation and speed was best correlated with profiles evoked by stimuli with the same orientation but at different speeds (i.e. correctly classified) as opposed to profiles evoked with the other line orientations also at different speeds (i.e. incorrectly classified). These analyses were performed with firing rate profiles conventionally represented in the temporal domain but also represented in the spatial domain where the positions of the underlying action potential are referenced to the position of the stimulus surface relative to the skin.

Precision of spiking responses

The hypothesis that subfield arrangement structures neurons’ spiking activity when edges pass over their receptive fields motivated us to estimate the spatial precision of generated action potentials with reference to neurons’ signaling of fine edge orientation differences. For this purpose, we used a correlation-based measure of similarity between pairs of individual spike trains after imposing various amounts of noise on the position of the recorded action potentials (Fellous et al., 2004; Schreiber et al., 2003). Thus, we convolved each spike train with Gaussians of different kernels with roughly logarithmically spaced SDs in the range of 5–1280 µm, which corresponds to increasingly blurring a neuron’s receptive field sensitivity topography (Jarocka et al., 2021). For each neuron and kernel width, we then pairwise correlated all smoothed spike trains and determined the spatial precision of action potentials by calculating which kernel width yielded the largest average difference in correlations with the same orientations at other speeds and with different orientations at different speeds (termed ‘best kernel’). This analysis thus only considered correlations between speed conditions since our hypothesis predicts speed-invariant edge orientation signaling when spike trains are represented in the spatial domain.

For spike trains convolved with the best kernel averaged across speeds for the FA-1 and SA-1 neurons, respectively, we also quantified for each neuron type the proportion of correctly classified orientations across scanning speeds analogous to the analysis of speed-invariant orientation signaling with firing rate profiles (chance performance = 0.25). Finally, based on pairwise comparisons between the available stimuli, we quantified the proportion of correctly classified orientations as a function of angular difference (chance = 0.5). Given our dataset, the available angular differences were 5 (between –5 and –10° line stimuli and between +5 and +10° line stimuli), 10 (–5 and +5°), 15 (–10 and +5°; –5 and +10°), and 20° (–10 and +10°).

Statistics

We carried out all statistical analysis in R Studio (V1.2.5) and SPSS (V29). Details of statistical analysis are provided in the text including the degrees of freedom, the test statistic, the p-value, and the effect size (partial eta squared: ηp2). In all cases, kernel width data was log-transformed, and discrimination probability data was arcsine square root transformed where needed. For ANOVAs evaluating discrimination probability, we subtracted chance from the data such that a significant intercept in the ANOVA model meant that overall performance was above chance. These analyses included scanning speed (and sometimes orientation difference) as a within-group (repeated measures) factor and neuron type as a between-group factor.

Results

We examined the capacity of individual FA-1 and SA-1 neurons innervating human fingertips to signal fine orientation differences of edges moving across their receptive field as a function of scanning speed as well as their ability to signal fine orientation differences in a speed-invariant manner. We did so with respect to different aspects of their spiking responses. These included traditional intensity measures (i.e. peak and mean firing rate) as well as spatiotemporal measures based on the sequential structure of the spike trains. For the sequential structure, our emphasis was on its representation in the spatial domain where each action potential is referenced to the position of the edge stimulus on the skin when it occurred as this relates to the spatial structuring of the neuron’s receptive field in the form of subfields.

We recorded action potentials from 30 FA-1 and 23 SA-1 isolated neurons when stimulated by repeatedly sweeping raised lines across their receptive field (Figure 1A). The lines were oriented ±5 and ±10° relative to their direction of motion. All neurons were stimulated at eight speeds from 15 to 180 mm/s. Each edge orientation was presented 15 times per speed. The contact force between the stimuli and the finger was maintained at ~0.4 N, which is typical for haptic exploration with such stimuli (Olczak et al., 2018). Figure 1B shows the response of an exemplar neuron to the edges as a function of scanning speed. The scanning speed affected neurons’ responses as expected. That is, mean firing rate (Figure 2A) and peak firing rate (Figure 2B) increased with speed, and the number of spikes decreased with speed (Figure 2C); in general, FA-1 neurons responded with higher rates than SA-1 neurons.

Response intensity as a function of stimulation speed.

(A) Mean firing rate averaged across fast-adapting type 1 (FA-1) neurons (red line) and slow-adapting type 1 (SA-1) neurons (blue line), respectively. The error bars represent the SEM across neurons (FA-1: n=30; SA-1: n=23). The thin gray lines represent mean responses for each of the four line stimuli separately. (B and C) Same format but for peak firing rate and spike count, respectively.

Orientation signaling as a function of scanning speed

For each scanning speed, we calculated how well an ideal observer could correctly classify an edge orientation based on a given neuron’s peak and mean firing rate (see Methods). Discrimination accuracy based on both of these measures was poor but above chance across the speeds (Figure 3A, B). That is, separate two-way ANOVAs with speed as a within-group (repeated measures) factor and neuron type as a between-group factor were applied to the proportion of correctly classified edge orientations referenced to chance performance (25%) and revealed a significant intercept for both peak (F1,51 = 556.9, p<0.001, ηp2=0.92) and mean (F1,51 = 196.3, p<0.001, ηp2=0.79) firing rate.

Within-speed discrimination accuracy.

Mean within-speed discrimination accuracy for fast-adapting type 1 (FA-1) neurons (red lines) and slow-adapting type 1 (SA-1) neurons (blue lines) as a function of simulation speed based on neuron’s mean firing rate (A), peak firing rate (B), and firing rate profile (C). (A – C) Shaded areas illustrate the SEM and dashed line chance discrimination performance (25%).

For peak firing rate, mean discrimination accuracy averaged across neurons and scanning speeds was 42% (range as a function of speed: 35–44%) for FA-1 neurons and 39% (range: 35–44%) for SA-1 neurons. We found no significant effect of neuron type on discrimination accuracy (F1,51 = 1.7, p=0.20, ηp2=0.03) but observed a reliable effect of speed (F7,357 = 3.4, p<0.01, ηp2=0.06) indicating a tendency for worse discrimination with increasing speed. For mean firing rate, mean discrimination accuracy averaged across the scanning speeds was 39% (range as a function of speed: 32–44%) for FA-1 neurons and 36% (range: 33–39%) for SA-1 neurons. Again, we found no significant effect of neuron type on discrimination accuracy (F1,51 = 2.6, p=0.11, ηp2=0.05) but an effect of speed (F7,357 = 4.1, p<0.001, ηp2=0.07) indicating a tendency for worse discrimination with increasing speed. For both peak and mean firing rate, the effect of speed might relate to the decreasing number of action potentials generated per edge stimulus at higher speeds.

Given that edge orientation signaling can occur via changes in the sequential structure of a neuron’s spiking response (Pruszynski and Johansson, 2014), we calculated how well an ideal observer could correctly classify edge orientation based on a neuron’s firing rate profile as a function of scanning speed (see Methods). Discrimination accuracy based on firing rate profile was substantially higher than that based on intensity measures (Figure 3C). Averaged across the speeds, mean discrimination accuracy was 78% for FA-1 neurons (range as a function of speed: 45–95%) and 51% for SA-1 neurons (range: 29–62%). A two-way ANOVA applied to the proportion of correctly classified edge orientations revealed a significant intercept referenced to chance (25%) performance (F1,51 = 309.5, p<0.001, ηp2=0.86). Discrimination accuracy increased with decreasing speed (F7,357 = 71.1, p<0.001, ηp2=0.58) and was, on average, better for FA-1 neurons than for SA-1 neurons (F1,51 = 33.2, p<0.001, ηp2=0.40). The superiority of FA-1 neurons was at low to moderate speeds, while the discrimination accuracy of both neuron types approached chance performance at the highest speeds, giving rise to a significant interaction between speed and neuron type (F7,357 = 6.5, p<0.001, ηp2=0.11).

These results show that the sequential structure of individual first-order tactile neuron responses contains considerable information about fine edge orientation differences. However, it is not obvious that neuron subfield arrangements are a crucial factor in the structuring of responses in this regard. That is, first-order tactile neurons can be incredibly sensitive to skin distortions and produce remarkably repeatable spiking responses with temporal precision to the millisecond level, so it may be argued that edge orientation discrimination can be based on any difference in mechanical events produced by non-identical stimuli (Suresh et al., 2016).

Speed-invariant orientation signaling

The following section deals with the ability of individual first-order tactile neurons to precisely signal information about edge orientation in a speed-invariant manner. We are motivated to investigate speed invariance because speed often varies when touching and manipulating objects but mostly because it provides one means of testing whether the subfield arrangement of FA-1 and SA-1 neurons is an important factor for their signaling of fine differences in edge orientation.

Based on a neuron’s response to a line stimulus recorded at a given speed, we calculated how well an ideal observer could identify the correct edge orientation based on the neuron’s responses at the other speeds (see Methods). In this scenario, the best-case outcome is that across-speed discrimination performance is equivalent to within-speed discrimination performance for a given speed condition. As expected, given how strongly intensity measures (i.e. peak and mean firing rates) are affected by the scanning speed (Figure 2), these measures failed to provide any reliable information about edge orientation at other scanning speeds (intercept of two-way ANOVA referenced to chance performance; peak firing rate: F1,51 = 0.09, p=0.77, ηp2=0.002; mean firing rate: F1,51 = 0.07, p=0.72, ηp2=0.001; Figure 4A, B). On average, FA-1 and SA-1 neurons carried little information about fine orientation differences even under the assumption that scanning speed is accessible based on the overall intensity of the population response. That is, even after compensating for the effect of scanning speed on a neuron’s response by normalizing its response by a linear function approximating the population level speed-intensity relationship (Figure 2), the neurons failed to provide reliable information about edge orientation (peak firing rate: F1,51 = 0.16, p=0.69, ηp2=0.003; mean firing rate: F1,51 = 2.7, p=0.1, ηp2=0.051). Also as expected, given that a neuron’s spike train is compressed and dilated in time as a function of speed (Figure 1B), firing rate profiles represented in the temporal domain did not retain reliable information about edge-orientation at other stimulation speeds (F1,51 = 0.59, p=0.44, ηp2=0.01; Figure 4C).

Across-speed discrimination accuracy.

Mean across-speed discrimination accuracy for fast-adapting type 1 (FA-1) neurons (red lines) and slow-adapting type 1 (SA-1) neurons (blue lines) as a function of core speed based on neuron’s mean firing rate (A), peak firing rate (B), firing rate profile represented in the temporal domain (C), and in the spatial domain (D). (A – C) Shaded areas illustrate the standard error around the mean (FA-1: n=30; SA-1: n=23) and dashed horizontal line chance discrimination performance (25%).

In contrast, firing rate profiles contained substantial information about edge orientation at other speeds if represented in the spatial domain and therefore not compressed or dilated with speed changes (i.e. when the position of each action potential is referenced to the position of the edge relative to the skin; Figure 4D). Averaged across the speeds, mean discrimination accuracy was 41% (range as a function of speed: 29–48%) among FA-1 neurons and 32% (range: 28–35%) among SA-1 neurons. A two-way ANOVA referenced to chance performance (25%), with speed as a within-group factor and neuron type as a between-group factor, revealed a significant intercept (F1,51 = 119.2, p<0.001,ηp2=0.70). Across-speed discrimination accuracy was reliably better for FA-1 neurons than for SA-1 neurons (F1,51 = 14.2, p<0.001, ηp2=0.22). Discrimination accuracy also varied with speed (F7,357 = 41.9, p<0.001, ηp2=0.45), with a decrease in performance for higher speeds presumably related to the relative decrease in the overall number of action potentials per stimulus. A significant interaction between speed and neuron type (F7,357 = 6.5, p<0.01, ηp2=0.11) reflected a higher increase in discrimination accuracy with decreasing speed for the FA-1 than for SA-1 neurons.

Spatial precision of spike responses

The analysis above indicates that the spatial structuring of FA-1 and SA-1 responses is fairly stable over scanning speeds, which supports our hypothesis that neurons’ subfield arrangements give rise to their ability to signal fine edge orientation differences. However, it does not establish whether the spatial precision of the generated action potentials aligns with the spatial acuity of the subfield arrangement of FA-1 and SA-1 neurons (Jarocka et al., 2021).

We assessed the spatial precision of the spiking responses by applying varying amounts of noise to the position of the recorded action potentials and quantifying how the noise affected the neuron’s across-speeds signaling of edge orientation. In practice, each individual spike train was convolved with Gaussian kernels of various widths (5–1280 µm) that increasingly attenuated the spatial structure of the response (Figure 5A) analogous to blurring the spatial acuity of the neuron’s subfield arrangement. For each kernel width, we then correlated each of the convolved spike trains obtained for a given edge orientation with (1) the spike trains obtained with the same orientation at all other speeds and (2) the spike trains obtained with the other orientations at all other speeds.

Spatial precision of spike responses.

(A) Illustration of the kernel convolution procedure for an exemplar neuron for one speed and all four line stimuli. Colored traces within each panel show the spike train smoothed by a Gaussian kernel of the indicated SD. Note how increasing the kernel width progressively blurs the representation of where individual action potentials occur. (B) Top: mean correlation (expressed as R2) between neuron responses to the same edge orientations (thick line) and different edge orientations (thin line) as a function of kernel width for the 30 mm/s scanning speed. Bottom: average correlation difference between stimuli with the same and with different edge orientations as a function of kernel width for the 30 mm/s scanning speed. Red and blue lines represent fast-adapting type 1 (FA-1) and slow-adapting type 1 (SA-1) neurons, respectively. Shaded areas illustrate the standard error around the mean. (C) Cumulative frequency distribution of the best kernel of FA-1 (red) and SA-1 (blue) neurons where each neuron is represented by the mean value of the best kernel calculated across speeds. (D) Mean best kernel as a function of scanning speed for FA-1 (red line) and SA-1 neurons (blue line). Shaded areas illustrate the standard error around the mean (FA-1: n=30; SA-1: n=23).

In either case, similarly low correlations arose with the narrowest kernels because the spike jitter between repetitions of the same edge-speed combination tended to be greater than the kernel width. With increasing kernel width, the correlation gradually increased indicating that the spike trains became more similar; the correlation approached one with the widest kernels because all stimulus-dependent structuring of the spike trains was then practically blurred (Figure 5B, upper panel). At intermediate kernel widths, however, the correlations between the convolved spike trains originating from different edge orientations were smaller than corresponding correlations between spike trains originating from the same orientations. At these kernel widths, the spike trains from different edge orientations were more different with respect to features sensitive to edge orientation differences. Hence, the kernel width at which the difference between these two sets of correlations is greatest provides an estimate of the spatial precision of action potentials critical for edge orientation signaling across speeds.

For each neuron and kernel-speed combination, we averaged the correlations involving trials with the same and different edge orientations and then calculated the difference between these averages. This difference was unimodal and followed an inverted-U profile as a function of kernel width, approaching zero when kernels were very narrow and wide (Figure 5B, lower panel). The maximum value therefore provided a distinct estimate of a neuron’s spatial precision as a function of speed.

The distribution of best kernels averaged across scanning speeds was narrow with a mean value of 66 µm for FA-1 neurons and 73 µm for SA-1 neurons (Figure 5C). A two-way ANOVA showed a main effect of scanning speed (F7,357 = 8.38, p<0.001, ηp2=0.141) but not of neuron type (F1,51 = 0.98, p=0.32, ηp2=0.019). There was also a significant interaction between the effects of these factors (F7,357 = 8.4, p<0.001, ηp2=0.142) as SA-1 kernels were less sensitive to scanning speed (Figure 5D). Notably, the estimates of spatial precision based on fine edge orientation discrimination are very similar to the estimates of spatial acuity of the subfield arrangements for FA-1 and SA-1 neurons (see Jarocka et al., 2021 and Discussion).

Discrimination accuracy based on convolved spike trains

Here, we examined across-speed discrimination accuracy based on convolved spike trains. We calculated discrimination accuracy across the speeds after convolving the spike trains with the mean of the best kernel across FA-1 and SA-1 neurons averaged across the speeds (66 and 73 µm, respectively). We justified the use of a single kernel given the relatively narrow distribution of best kernels across neurons of each type, and the fact that for individual neurons, the exact kernel had a modest effect on the calculated dissimilarity of the relevant spike trains. Note that the corresponding analyses based on the best kernels specified for each neuron individually showed virtually the same results.

Across speed discrimination accuracy based on convolved spike trains was similar to that based on firing rate profiles represented in the spatial domain for SA-1 neurons and for FA-1 neurons it was overall better (Figure 6A). Mean discrimination accuracy was 48% (range as a function of speed: 34–55%) for FA-1 neurons and 32% (range: 26–36%) for SA-1 neurons. A two-way ANOVA applied to the proportion of correctly classified edge orientations revealed a significant intercept referenced to chance (25%) performance (F1,51 = 92.2, p<0.001, ηp2=0.64) and main effects of neuron type (F1,51 = 20.5, p<0.001, ηp2=0.29) and speed (F7,357 = 60.3, p<0.001, ηp2=0.54). On average across neurons, the discrimination accuracy was generally better for FA-1 than for SA-1 neurons. Regarding the speed effect, for both neuron types, the discrimination accuracy was fairly uniform for speeds up to 45 mm/s, after which it gradually decreased with increasing speed. This decrease was stronger for FA-1 than for SA-1 neurons, which rendered a significant interaction between speed and neuron type (F7,357 = 8.7, p<0.01, ηp2=0.15).

Across-speed discrimination accuracy based on smoothed spike trains.

(A) Average across-speed discrimination accuracy for fast-adapting type 1 (FA-1) (red line) and slow-adapting type 1 (SA-1) neurons (blue line) as a function of scanning speed. (B) Same format but considering only the speeds commonly used by people when discriminating these kinds of stimuli (15–45 mm/s). (A and B) Data based on spike trains convolved by the average best kernel over neurons and speeds (66 μm for FA-1 neurons, n=30; 73 μm for SA-1 neurons, n=23). Shaded areas illustrate the SEM, and the dashed line indicates chance discrimination performance (25%).

From a behavioral perspective, the above analyses of speed-invariant signaling include scanning speeds that span a much wider range (15–180 mm/s) than what people commonly use (15–45 mm/s) when discriminating similar stimuli (Olczak et al., 2018; Vega-Bermudez et al., 1991). Therefore, to provide a more pragmatic view on neurons’ speed-invariant signaling, we calculated across speed discrimination accuracy limited to the range of these commonly used speeds (Figure 6B). When considering the commonly used speeds, edge orientation discrimination accuracy was strikingly high, averaging 75% (range as a function of speed: 72–77%) for FA-1 neurons and 43% (range: 39–45%) for SA-1 neurons. A two-way ANOVA focusing on the commonly used speeds revealed a significant main effect of speed (F3,153 = 12.5, p<0.001, ηp2=0.20) and neuron type (F1,51 = 5.6, p<0.001, ηp2=0.33) but no significant interaction (F3,153 = 0.13, p=0.72). That is, the superiority of FA-1 neurons was uniform across the commonly used speeds.

Our analyses so far have concerned data pooled over all the edge orientation differences present on our stimulation surface (Δθ=5, 10, 15 and 20°, see Methods). Discrimination accuracy for all combinations of edge orientations and speeds is illustrated as a confusion matrix in Figure 7. Note that the various line stimuli were similarly discriminated and that misclassifications tended to occur toward edges with similar orientation (i.e. off-diagonal terms). To directly address how orientation difference influences the ability of FA-1 and SA-1 neurons to signal edge orientation, we examined how across-speed discrimination accuracy was affected by orientation difference for each pair-wise orientation difference and speed (chance = 0.5). As expected, discrimination accuracy increased gradually with increasing orientation difference (Figure 8). A three-way ANOVA with orientation difference and speed as within-group (repeated measures) factors and neuron type as between-group factor indicated a main effect of orientation difference on discrimination accuracy (F3,153 = 35.2 p<0.001, ηp2=0.41). The superiority of FA-1 neurons over the SA-1 neurons remained (F1,51 = 15.0, p<0.001, ηp2=0.23) as did the observation that discrimination accuracy varies with speed (F7,357 = 21.4, p<0.001, ηp2=0.30). This effect appeared consistent across neuron types as we found no significant interaction between scanning speed and neuron type (F7,1071 = 1.5, p=0.15, ηp2=0.03). We did, however, find a significant interaction between scanning speed and orientation difference (F21,1071 = 1.8, p<0.05, ηp2=0.03) consistent with the observation that the effect of orientation difference on discrimination accuracy decreased with increasing speed.

Confusion matrix showing discrimination across all experimental conditions.

Discrimination accuracy for all edge orientation and speed combinations based on spike trains convolved by the average best kernel over neurons and speeds (66μm for fast-adapting type 1 [FA-1] neurons, n=30; 73μm for slow-adapting type 1 [SA –1], n=23). Each 4×4 submatrix represents comparisons of the four oriented edges within the indicated speed combination. Thus, the main diagonal of the submatrices represents within-speed comparisons, and the off-diagonal submatrices represent across-speed comparisons. The brightness of each element represents the probability of assigning the correct edge orientation to each of the four possibilities where chance performance is 25%.

Discrimination accuracy as a function of orientation difference.

Mean across-speed discrimination accuracy for fast-adapting type 1 (FA-1) (red lines) and slow-adapting type 1 (SA-1) neurons (blue lines) as a function of speed and orientation difference. Data based on spike trains convolved by the average best kernel over neurons and speeds (66 μm for FA-1 neurons, n=30; 73 μm for SA –1, n=23). The different shades of each color represent the indicated orientation differences. Error bars represent the SEM, and the dashed line indicates chance discrimination performance (50%).

Lastly, given our interest in signaling very fine edge orientation differences, we asked at what scanning speeds individual FA-1 and SA-1 neurons could discriminate the finest orientation difference in our dataset (Δθ=5°). That is, for each neuron type, we examined if discrimination accuracy exceeded chance performance (50%) by using a one-tailed one-sample t-tests for each speed followed by Bonferroni correction for multiple comparison (N=8). For FA-1 neurons, discrimination accuracy exceeded chance performance for all but the highest two speeds (t29≥4.41, pcorrected≤0.005 for speeds ≤90 mm/s; t29<2.6, pcorrected>0.06 for speeds >90 mm/s). And for SA-1 neurons, discrimination accuracy exceeded chance performance for all but the three highest speeds (t22≥3.2, pcorrected≤0.02 for speeds from ≤60 mm/s; t22≤1.8, pcorrected≥0.38 for speeds >60 mm/s). Thus, at scanning speeds that are commonly used in tactile spatial discrimination tasks, individual neurons of both types can contribute speed-invariant information about edge orientation at a level of detail corresponding to differences of only 5°.

Discussion

We show that individual FA-1 and SA-1 neurons carry substantial information about fine orientation differences in the sequential structure of their spike trains but little in the intensity of their responses. Moreover, the sequential structure allows speed-invariant signaling of fine edge orientation differences across a wide range of scanning speeds when represented in the spatial domain, when action potentials are referenced to the position of the edge stimulus on the skin. In contrast, response intensity from an individual neuron appears informative only when it is stimulated with a uniform scanning speed.

Our findings reveal that single human first-order tactile neurons signal edge orientation most reliably at slow to moderate speeds, corresponding to those used when actively performing tactile spatial discrimination tasks with the fingertips (Olczak et al., 2018; Vega-Bermudez et al., 1991). Our findings also reveal that edge orientation signaling decreases for stimulation speeds exceeding ~45 mm/s, corresponding to the speeds at which spatial discrimination capacity decreases (Bensmaia et al., 2008; Vega-Bermudez et al., 1991). We speculate that the choice of speeds in active tasks and changes in discrimination capacity as a function of speed in passive tasks are associated with the ability of first-order tactile neurons to produce the relevant information. Edge orientation sensitivity at natural speeds was strikingly high, with neurons routinely showing speed-invariant orientation signaling for differences as small as 5°, which is substantially better than what humans can consciously report (Bensmaia et al., 2008; Lechelt, 1992; Olczak et al., 2018; Peters et al., 2015) but on a par with what people exhibit in object manipulation (Pruszynski et al., 2018).

The edge orientation sensitivity we report might underestimate the spatial sensitivity of FA-1 and SA-1 neurons. First, many first-order tactile neurons could reliably discriminate even our most finely spaced edge orientations. Second, our analysis ignored the viscoelastic and anisotropic properties of the fingertip, and thus possible complex time-varying deformations of the fingertip skin as a surface slide over it (Delhaye et al., 2016; Jarocka et al., 2021). Varying amounts of compression, stretching, and shearing of the skin within a neuron’s receptive field may therefore have occurred during the repeated scans both within and across speeds and between edges with different orientations. Such events would have erroneously increased our estimate of the spatial jitter of action potentials across the repetitions of a given stimulus and thus erroneously reduced our estimate of edge orientation information in the spike trains.

For stimuli with larger orientation differences (≥22.5°), we previously showed that a neuron’s sensitivity to edge orientation was linked to the layout of subfields in its receptive field (Pruszynski and Johansson, 2014). That is, we could predict the structure of the generated spike trains (in terms of the presence of spikes and the lengths of gaps between spike bursts) as a function of edge orientation based on how the neuron’s subfields were sequentially stimulated. In the present study, we could not directly relate a neuron’s edge orientation signaling to the sensitivity topography of the receptive field because we did not collect receptive field maps. Nevertheless, two pieces of evidence suggest that the mechanism is the same. First, that a neuron exhibits speed invariance in signaling fine edge orientation differences especially at natural scanning speeds is consistent with its responses being structured by its subfield layout, which for FA-1 and SA-1 neurons, is rather speed invariant at least for speeds up to 60 mm/s (Jarocka et al., 2021). Second, in terms of Gaussian kernel widths, the estimate of the spatial precision of action potentials in this study is practically identical to the estimated spatial sensitivity of the subfield arrangement of the FA-1 and SA-1 neurons mapped by small dots laterally scanning the receptive field (Jarocka et al., 2021). For both studies, the estimated spatial sensitivity implies a spatial period expressed in sinusoidal terms that basically matches the width of an individual papillary ridge. That is, based on a Gaussian function being very similar to the period of a cosine cycle specified between −π and π, the spatial sensitivity in Gaussian terms averaged across both neuron types in the two studies corresponds to a sinusoidal period of 0.33 mm and 0.41 mm, respectively (for details, see Jarocka et al., 2021). One possibility is that the width of the line stimuli could have acted as a low-pass filter and thus led to an underestimation of spatial sensitivity. However, we do not believe this to be the case given previous work showing that ridge deflections that stimulate FA-1 and SA-1 neurons are largely driven by the leading edge of similar stimuli and that the neurons, as well as their monkey equivalents, generally respond more intensely to the leading rather than trailing ends of tactile elements (Blake et al., 1997; LaMotte and Whitehouse, 1986; Pruszynski and Johansson, 2014). FA-1 neurons were generally better than SA-1 neurons in signaling fine edge orientation based on the spatial structuring of their responses. A plausible explanation is that the receptive fields of human FA-1 neurons have about twice as many subfields as SA-1 neurons (Jarocka et al., 2021; Johansson, 1978; Phillips et al., 1992), while the overall receptive field sizes are comparable (Johansson and Vallbo, 1980). A larger number of subfields could in principle provide higher sensitivity to fine orientation differences by offering more varied sequential structuring with orientation changes (Pruszynski et al., 2018). The superiority of FA-1 neurons in this regard, together with their density in the fingertip being approximately twice as high as that of the SA-1 neurons (Johansson and Vallbo, 1979), suggests that FA-1 neurons play a primary role in extracting detailed spatial properties associated with dynamic fingertip events. Indeed, FA-1 neurons (or those like FA-1 neurons) dominate innervation in other body areas, such as the lips/tongue (Trulsson and Essick, 1997) and foot sole (Corniani and Saal, 2020), where the high-fidelity extraction of dynamic mechanical events seems key to their function. In contrast, the low-threshold mechanoreceptive innervation of the human hairy skin appears dominated by slowly adapting neurons (Corniani and Saal, 2020).

Our study highlights that individual FA-1 and SA-1 neurons can signal fine edge orientation differences via the sequential structure of evoked spike trains. However, for a host of reasons, it seems unlikely that the central nervous system would rely on registering the sequential structure of spike trains in individual neurons to estimate edge orientation. For example, such a scheme would require the spike train to be considered in its entirety which would take a long time, especially at slower scanning speeds due to the sizable spread of a neuron’s subfields over the skin, and it would not function at all without lateral scanning motion. And for speed invariant signaling of edge orientation information, it would require that the nervous system have knowledge about the time course of each spike train. Similar limitations would also apply to the intensity measures, and in addition, accurate time-varying instantaneous information about scanning speed would be required for these measures to contribute to speed invariant signaling of edge orientation.

We have instead proposed a population level mechanism for geometric feature signaling that utilizes the high-spatial precision of spike generation offered by the presence FA-1 and SA-1 neuron subfields and which can signal tactile spatial details both instantaneously and in a speed-invariant manner (Hay and Pruszynski, 2020; Pruszynski et al., 2018; Pruszynski and Johansson, 2014). Briefly, the basis is that subfields belonging to different neurons are highly intermingled because of the high density of FA-1 and SA-1 neurons, especially in the fingertip skin (Johansson and Vallbo, 1979), and the substantial size of the skin area in which a neuron’s subfields are distributed (Jarocka et al., 2021; Johansson and Vallbo, 1980). Consequently, when an edge of a certain orientation deforms the skin at a particular location, a subset of neurons whose subfields spatially coincide with the edge is synchronously excited, but at a slightly different orientation or location, synchrony occurs primarily in another subset of neurons. In this scheme, the degree to which stimuli synchronously engage different subsets of neurons determines the ultimate edge orientation resolution of the nervous system as well as its resolution in the case of discrimination of tactile spatial details in general. Our previous simulations based on realistic innervation density and receptive field sizes suggest that the presence of subfields is particularly beneficial for edge-orientation signaling for short edges that involve only a small part of the fingertip which might be particularly relevant for the precise control of the digits in tasks requiring dexterous object manipulation (Pruszynski et al., 2018). Regarding the conscious perception of edge orientation, when edges of different lengths are pressed against a fingertip, humans perform poorly for 2 mm long edges followed by an abrupt improvement for edges longer than 4 mm (Peters et al., 2015). It is tempting to speculate that the substantial performance degradation for 2 mm long edges relates to the fact that for a vast majority of FA-1 and SA-1 neurons, this length is substantially smaller than the area of the fingertip skin where a neuron’s subfields are distributed (Jarocka et al., 2021; Johansson, 1978; Phillips et al., 1992). That is, if the edge length is smaller than the scale of the subfield arrangement, a single neuron likely carries less information about edge orientation compared to edge lengths can stimulate the entire set of subfields.

The present study cannot ultimately determine how the nervous system builds and propagates information about the high-dimensional space of stimulus features required for doing object manipulation or exploratory tasks. However, the anatomical and functional organization of tactile ascending pathways, as well as sensory pathways in general, suggest that synchronized activity across neuronal ensembles induced by sensory events may play a key role (Brette, 2012; Bruno, 2011; Pruszynski and Zylberberg, 2019; Salinas and Sejnowski, 2001; Singer, 1999; Stanley, 2013). A hallmark of sensory ascending pathways, including tactile pathways, is massive increases in divergence and convergence as excitatory connections synapse sequentially and downstream representations engage orders of magnitude more neurons than the incoming axons (Babadi and Sompolinsky, 2014; Jones, 2000). This dimensionality expansion together with the subfield organization of first-order neurons and the sensitivity of neuronal activity to the timing of synaptic input would offer exceptional potential for simultaneous high-resolution identification of various tactile spatiotemporal events, based on patterns of correlated inputs across neurons. Furthermore, different synaptic integration times imply that both the precise timing of individual spikes in presynaptic axons as well as the intensity of spike bursts could contribute depending on the time scales of the sensory inputs and of the nature and context of the current tasks (Harvey et al., 2013; Hay and Pruszynski, 2020; Lankarany et al., 2019). For object manipulation that requires rapid information about changes in spatial contact states, the nervous system could primarily rely on the precise timing of input across neuronal ensembles, while in perceptual judgment tasks, that usually take place under less time pressure allowing more time for synaptic integration, information in individual neurons’ spike intensities could play a greater role (Hay and Pruszynski, 2020; Johansson and Birznieks, 2004; Pruszynski et al., 2018). It is well established that the central processing of sensory information in general can depend on task and context (Crapse and Sommer, 2008; Engel et al., 2001; Gazzaley and Nobre, 2012; Manita et al., 2015; Schroeder et al., 2010; Zagha et al., 2013). For the somatosensory system, descending motor-related signals at different levels of the sensorimotor system can affect virtually all levels of the tactile processing pathways (Adams et al., 2013; Canedo, 1997; Fanselow and Nicolelis, 1999; Ghez and Pisa, 1972; Lee et al., 2008; Manita et al., 2015; Seki and Fetz, 2012; Zagha et al., 2013). A seemingly effective way to adapt tactile feature computations as a function of behavioral goals would be for descending mechanisms to act on synapses of the tactile ascending pathways to influence the exact pattern of synchronized activity across the neuronal ensembles that are propagated. The plausibility of this assumption, we believe, will be clarified by recent advances in recording neural activity from earliest stages of tactile processing in the spinal cord (Confais et al., 2017) and brainstem of awake and behaving animals (Conner et al., 2021; He et al., 2021; Versteeg et al., 2021).

Data availability

Al the raw data generated as part of this study are publicly available.

The following data sets were generated
    1. Sukumar V
    2. Johansson R
    3. Pruszynski J
    (2022) Dryad Digital Repository
    Precise and stable edge orientation signaling by human first-order tactile neurons.
    https://doi.org/10.5061/dryad.r7sqv9sgf

References

    1. Vallbo AB
    2. Johansson RS
    (1984)
    Properties of cutaneous mechanoreceptors in the human hand related to touch sensation
    Human Neurobiology 3:3–14.

Decision letter

  1. Hannes P Saal
    Reviewing Editor; University of Sheffield, United Kingdom
  2. Tamar R Makin
    Senior Editor; University of Cambridge, United Kingdom
  3. Hannes P Saal
    Reviewer; University of Sheffield, United Kingdom
  4. Benoit Delhaye
    Reviewer; Université Catholique de Louvain, Belgium
  5. Saad S Nagi
    Reviewer; Linköping University, Sweden

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Precise and stable edge orientation signaling by human first-order tactile neurons" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Hannes P Saal as Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Tamar Makin as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Benoit Delhaye (Reviewer #2); Saad S Nagi (Reviewer #3).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) Reviewer #1 questioned whether the comparison between rate and spike-timing based codes was fair, as the spike-timing code was adjusted for scanning speed, while the rate code was not. Please address this point in the revision; this will likely require additional analysis.

2) Please add a section in the Discussion providing a more in-depth comparison with the psychophysical literature (see comment by reviewer #1).

3) Several reviewers had questions about the stimulus set (choice of stimuli, inclusion of some conditions in the results but not others). Please clarify these in the text.

4) Reviewer #2 asked about the link between the spatial precision of the neurons and the size of a fingerprint ridge. In a somewhat related question, reviewer #1 wondered about the robustness of sub-field responses especially during higher scanning speeds. Please comment on these issues.

Reviewer #1 (Recommendations for the authors):

1) In the comparison across different speeds, the temporal profile is corrected for scanning speed, while the spike rates are not. To make a fairer comparison while assuming that scanning speed is accessible, one might fit a linear function to the speed/rate data (see Figure 2) and then 'correct' the rates accordingly. Any systematic deviation in firing rates due to the different orientations should then be discriminable. Given that firing rates do not perform particularly well even in the within-speed discrimination analysis, I would not expect them to outperform the spatial profile in the across-speed analysis either, but such an analysis would provide more robust evidence that rates alone are not sufficient to accomplish fine discrimination.

2) Why was a vertical orientation (0 degrees) not included in the setup? The authors cite 10 degrees as the perceptual lower bound, but this value cannot be directly tested due to the omission of a vertically oriented edge.

3) The comparison with the psychophysical literature could be more extensive. For example, the Bensmaia paper that is cited in the introduction also investigated perceptual thresholds across different scanning speeds, a question that is relevant for the current paper as well. The Peters study tested different edge lengths, which might be compared against the size of the typical receptive field etc.

4) Figure 2: At the highest scanning speeds, the average spike count drops to below 10 spikes. Presumably here not every sub-field does reliably elicit a spike anymore. Are the few spikes that do get elicited randomly distributed across all subfields, or do certain sub-fields reliably produce spikes while others drop out completely? That is, is the signal robust but not strong enough to aid in discrimination, or is the robustness lost?

5) Figure 7 and associated analysis: In the confusion matrices, when orientations are mis-classified, does this appear most often with their closest orientation? Such smoothness would be expected from the interaction of the sub-fields with the differently oriented lines.

Reviewer #2 (Recommendations for the authors):

I only have a few comments:

- The data collected at 2.5, 5, and 10 mm/s does not appear in the results, is there a reason?

- The authors should probably elaborate on the link between the spatial precision of the neurons (~60-70 um) and the fingerprint ridges dimension (~300-400um), which I believe are not so close. Also, the edge widths are close to fingerprint ridges periodicity, could this have an artificial effect on the results?

- P16, first paragraph: Delhaye et al., 2019 is a review of primate somatosensory cortex and is not related to the sentence "the viscoelastic and anisotropic properties of the fingertip, and thus possible complex time-varying deformations of the fingertip skin as a surface slides over it".

Reviewer #3 (Recommendations for the authors):

This is a very well-written paper. I have no concerns and fully support its publication.

https://doi.org/10.7554/eLife.81476.sa1

Author response

Essential revisions:

1) Reviewer #1 questioned whether the comparison between rate and spike-timing based codes was fair, as the spike-timing code was adjusted for scanning speed, while the rate code was not. Please address this point in the revision; this will likely require additional analysis.

2) Please add a section in the Discussion providing a more in-depth comparison with the psychophysical literature (see comment by reviewer #1).

3) Several reviewers had questions about the stimulus set (choice of stimuli, inclusion of some conditions in the results but not others). Please clarify these in the text.

4) Reviewer #2 asked about the link between the spatial precision of the neurons and the size of a fingerprint ridge. In a somewhat related question, reviewer #1 wondered about the robustness of sub-field responses especially during higher scanning speeds. Please comment on these issues.

The above comments have been addressed. See below our response to specific reviewer comments for details.

Reviewer #1 (Recommendations for the authors):

1) In the comparison across different speeds, the temporal profile is corrected for scanning speed, while the spike rates are not. To make a fairer comparison while assuming that scanning speed is accessible, one might fit a linear function to the speed/rate data (see Figure 2) and then 'correct' the rates accordingly. Any systematic deviation in firing rates due to the different orientations should then be discriminable. Given that firing rates do not perform particularly well even in the within-speed discrimination analysis, I would not expect them to outperform the spatial profile in the across-speed analysis either, but such an analysis would provide more robust evidence that rates alone are not sufficient to accomplish fine discrimination.

As the reviewer suggests, the intensity measures could do better discriminating edge orientation across stimulation speeds with some ‘correction’ for stimulation speed. This is the case because, as we show in the paper, stimulation speed very strongly influences the intensity of FA-1 and SA-1 responses. In the limit, with perfect correction for stimulation speed, the neurons would do as well as they do in the within-speed comparison. Performance is already poor within-speed and hence the reviewer rightfully does not expect a correction to yield any qualitative change in interpretation with any correction. Of course, the nervous system cannot perform an ideal correction because it does not have direct access to the speed condition but rather must estimate stimulation speed via neural activity from (some set of) neurons in the population. This leads to the reviewer’s specific suggestion that we apply a correction according to the mean speed sensitivity of the population (as shown in Figure 2). We have done the requested correction which, on average, does not lead to reliable across-speed discrimination performance. The reason that correction by population level intensity information is not particularly effective is because the effect of speed is quite variable across neurons. We have also taken this comment as an opportunity to clarify to the reader why we are doing this analysis. We have made the following changes to the manuscript to help emphasize our analytical intentions and thus clarify the conclusions we believe can be drawn from this study.

“On average, FA-1 and SA-1 neurons carried little information about fine orientation differences even under the assumption that scanning speed is accessible based on the overall intensity of the population response. That is, even after compensating for the effect of scanning speed on a neuron’s response by normalizing its response by a linear function approximating the population level speed-intensity relationship (Figure 2), the neuron’s failed to provide reliable information about edge orientation (peak firing rate: F1,51 = 0.16, p = 0.69, ηp2 = 0.003; mean firing rate: F1,51 = 2.7, p = 0.1, ηp2 = 0.051).

(Results – Speed-invariant orientation signaling)

We are motivated to investigate speed-invariance because speed often varies when touching and manipulating objects but mostly because it provides one means of testing whether the subfield arrangement of FA-1 and SA-1 neurons is an important factor for their signaling of fine differences in edge orientation. (Results – Speed-invariant orientation signaling)

In this scenario, the best-case outcome is that across-speed discrimination performance is equivalent to within-speed discrimination performance for a given speed condition. (Results – Speed-invariant orientation signaling)

As expected, given how strongly intensity measures (i.e., peak and mean firing rates) are affected by the scanning speed (Figure 2), … (Results – Speed-invariant orientation signaling)”

2) Why was a vertical orientation (0 degrees) not included in the setup? The authors cite 10 degrees as the perceptual lower bound, but this value cannot be directly tested due to the omission of a vertically oriented edge.

We omitted a 0° stimulus to efficiently pack stimuli onto the drum which, as the reviewer knows, is critical for microneurography experiments due to the risk of losing isolation. With respect angular differences that can be tested, our present stimuli allow for comparing deltas of 5° degrees (+5° and +10°; -5° and -10° lines), 10° (-5° and +5°), 15° (-5° and +10°; +5° and -10°) and 20° (-10° and +10°). Adding a 0° line stimulus would have added more comparison options for these angular differences but would not yield additional angular differences per se. We now make this logic clear in the text.

“These orientations were chosen to efficiently pack the stimuli onto the stimulating surface and to permit pairwise comparison of lines with angular differences of 5° (between +5° and +10° line stimuli; between -5° and -10° line stimuli), 10° (between -5° and +5° line stimuli), 15° (between -5° and +10° line stimuli; between +5° and -10° line stimuli) and 20° (between -10° and +10° line stimuli). (Methods – Stimuli)

Given our data set, the available angular differences were 5° (between -5° and -10° line stimuli and between +5° and +10° line stimuli), 10° (-5° and +5°), 15° (-10° and +5°; -5° and +10°) and 20° (-10° and +10°). (Methods – Precision of spiking responses)”

3) The comparison with the psychophysical literature could be more extensive. For example, the Bensmaia paper that is cited in the introduction also investigated perceptual thresholds across different scanning speeds, a question that is relevant for the current paper as well. The Peters study tested different edge lengths, which might be compared against the size of the typical receptive field etc.

We agree and appreciate this push. We have now elaborated the relevant text in the Discussion.

“Our findings reveal that single human first-order tactile neurons signal edge orientation most reliably at slow to moderate speeds, corresponding to those used when actively performing tactile spatial discrimination tasks with the fingertips (Olczak et al., 2018; Vega-Bermudez et al., 1991). Our findings also reveal that edge orientation signaling decrease for stimulation speeds exceeding ~45 mm/s, corresponding to the speeds at which spatial discrimination capacity decreases (Bensmaia et al., 2008; Vega-Bermudez et al., 1991). We speculate that the choice of speeds in active tasks and changes in discrimination capacity as a function of speed in passive tasks are associated with the ability of first-order tactile neurons to produce the relevant information. Edge orientation sensitivity at natural speeds was strikingly high, with neurons routinely showing speed-invariant orientation signaling for differences as small as 5°, which is substantially better than what humans can consciously report (Bensmaia et al., 2008; Lechelt, 1992; Olczak et al., 2018; Peters et al., 2015) but on a par with what people exhibit in object manipulation (Pruszynski et al., 2018). (Discussion)

Regarding the conscious perception of edge orientation when edges of different lengths are pressed against a fingertip, humans perform poorly for 2 mm long edges followed by an abrupt improvement for edges longer than 4 mm (Peters et al., 2015). It is tempting to speculate that the substantial performance degradation for 2 mm long edges relates to the fact that, for a vast majority of FA-1 and SA-1 neurons, this length is substantially smaller than the area of the fingertip skin where a neuron's subfields are distributed (Jarocka et al., 2021; Johansson, 1978; Phillips et al., 1992). That is, if the edge length is smaller than the scale of the subfield arrangement, a single neuron likely carries less information about edge orientation compared to edge lengths can stimulate the entire set of subfields. (Discussion)”

4) Figure 2: At the highest scanning speeds, the average spike count drops to below 10 spikes. Presumably here not every sub-field does reliably elicit a spike anymore. Are the few spikes that do get elicited randomly distributed across all subfields, or do certain sub-fields reliably produce spikes while others drop out completely? That is, is the signal robust but not strong enough to aid in discrimination, or is the robustness lost?

The reviewer raises a very interesting question about the nature of subfield spike generation capacity and interactions among subfields – an issue we are presently tackling both empirically and computationally. The present experiment makes it difficult to say anything conclusive, however, since we do not have receptive field maps for these neurons. What is clear is that, over a rather large range, that basic subfield induced structure is maintained (which is why the across speed discrimination functions well for spikes aligned in spatial coordinated) but that this structuring breaks down for very high speeds. A simple (speculative) idea is that, when the speed is very high, the stimulus will traverse a subfield and generate an action potential, and then traverse other subfields while the neuron is still in its refractory period and thus not yield an action potential; in the extreme only one subfield will generate one action potential for all orientations and thus there would be no information about edge orientation in its response pattern. Of course, additional kinds of interactions are possible depending on the biophysics of these neurons, and these could, in principle, open up a host of computational advantages.

5) Figure 7 and associated analysis: In the confusion matrices, when orientations are mis-classified, does this appear most often with their closest orientation? Such smoothness would be expected from the interaction of the sub-fields with the differently oriented lines.

Generally, misclassifications happen to the closest orientations which can be seen in the off-diagonal terms of Figure 7. Note, however, that such smoothness is not an absolute necessity given a neuron’s response profile relates to the geometry of the subfield layout (see Pruszynski et al., eLife, 2018). Smoothness can also be seen in Figure 8 in the sense that smaller edge orientation differences are harder to discriminate than bigger edge orientation differences.

“Discrimination accuracy for all combinations of edge orientations and speeds is illustrated as a confusion matrix in Figure 7. Note that the various line stimuli were similarly discriminated and that misclassifications tended to occur towards edges with similar orientation (i.e. off-diagonal terms). (Results – Discrimination accuracy based on convolved spike trains)”

Reviewer #2 (Recommendations for the authors):

I only have a few comments:

- The data collected at 2.5, 5, and 10 mm/s does not appear in the results, is there a reason?

When we started collecting data for this study, we focused on the 15-180 mm/s speeds, which were presented in random order. Later in data collection, we added a 270 mm/s condition and later still, 2.5, 5, 10 mm/s speeds. These slowest three conditions were not randomized and were always collected at the end of data collection because the slow speeds take a very long time and thus we would often lose neuronal isolation before completion. Thus, the core data we have for all neurons is from 15-180 mm/s which gives us power in terms of number and the capacity to treat speed as a repeated measure. We have much lower numbers for the other conditions and thus limits and complicates statistical analysis. That said, these speeds show the expected trends and are included in the openly available dataset attached to this manuscript for future consideration. We now explain this explicitly in the Methods.

“All neurons were scanned at eight tangential speeds: 15, 20, 30, 45, 60, 90, 120, 180 mm/s presented in random order. A subset of neurons was scanned at up to four additional speeds: 2.5, 5, 10, and 270 mm/s. When the 270 mm/s speed was presented, it was randomly interleaved with the main eight speed conditions. When the three slowest speeds were presented, they were always done after all other speed conditions and in a set order, from fastest to slowest (i.e. 10 mm/s, 5 mm/s and then 2.5 mm/s). These very slow speeds take a long time to complete so presenting them at the end minimized the risk of losing isolation of the current neuron during the eight main speeds and thereby creating partial data sets. All the analyses presented here focus on the main speed conditions since these were available for all neurons. Note that the same general trends hold for the additional speeds and that the raw data for all speed conditions is provided alongside this manuscript. For each neuron and speed, the drum rotated three times, resulting in 15 trials for each line stimulus and recorded speed condition (5 presentations of stimuli per drum rotation x 3 drum rotations = 15 trials). (Methods – Stimuli).”

- The authors should probably elaborate on the link between the spatial precision of the neurons (~60-70 um) and the fingerprint ridges dimension (~300-400um), which I believe are not so close. Also, the edge widths are close to fingerprint ridges periodicity, could this have an artificial effect on the results?

Great comment, we agree this was unclear. We have previously dealt with these issues in detail in the context of receptive field maps using small dot stimuli (Jarocka et al., 2021) and the same concepts are in play here. That is, the spatial precision of action potentials reported (~70 um) corresponds to a sinusoid with a spatial period of (~350 um). In terms of the edge width being of similar scale to the ridges, we don’t believe this is likely given the leading edge of the stimulus is likely driving the deflection of ridges (Lamotte and Whitehouse, 1986) which we saw when recording from FA-1 and SA-1 neurons in our previous work in the sense that they responded more to leading than trailing ends of stimuli (Pruszynski and Johansson, 2014). Without fully rehashing the entire discussion from the Jarocka paper, we have added text to make these points and links explicitly.

“Indeed, the spatial precision of elicited action potentials rationally matched spatial acuity of subfield arrangements, which corresponds to a spatial period similar to the dimensions of individual fingertip ridges.” (Abstract)

“Second, in terms of Gaussian kernel widths, the estimate of the spatial precision of action potentials in this study is practically identical to the estimated spatial sensitivity of the subfield arrangement of the FA-1 and SA-1 neurons mapped by small dots laterally scanning the receptive field (Jarocka et al., 2021). For both studies, the estimated spatial sensitivity implies a spatial period expressed in sinusoidal terms that basically matches the width of an individual papillary ridge. That is, based on a Gaussian function being very similar to the period of a cosine cycle specified between -π and π, the spatial sensitivity in Gaussian terms averaged across both neuron types in the two studies corresponds to a sinusoidal period of 0.33 mm and 0.41 mm respectively (for details, see Jarocka et al., 2021). One possibility is that the width of the line stimuli could have acted as a low-pass filter and thus led to an underestimation of spatial sensitivity. However, we do not believe this to be the case given previous work showing that ridge deflections that stimulate FA-1 and SA-1 neurons are largely driven by the leading edge of similar stimuli and that the neurons, as well as their monkey equivalents, generally respond more intensely to the leading rather than trailing ends of tactile elements (Blake et al., 1997; LaMotte & Whitehouse, 1986; Pruszynski & Johansson, 2014).” (Discussion)

- P16, first paragraph: Delhaye et al., 2019 is a review of primate somatosensory cortex and is not related to the sentence "the viscoelastic and anisotropic properties of the fingertip, and thus possible complex time-varying deformations of the fingertip skin as a surface slides over it".

Our apologies, that was a typo, we have now corrected this citation to point to Delhaye et al, 2016 (https://doi.org/10.1098/rsif.2015.0874).

“Second, our analysis ignored the viscoelastic and anisotropic properties of the fingertip, and thus possible complex time-varying deformations of the fingertip skin as a surface slides over it (Delhaye et al., 2016; Jarocka et al., 2021).” (Discussion)

Reviewer #3 (Recommendations for the authors):

This is a very well-written paper. I have no concerns and fully support its publication.

We appreciate the positive feedback.

https://doi.org/10.7554/eLife.81476.sa2

Article and author information

Author details

  1. Vaishnavi Sukumar

    Neuroscience Graduate Program, Western University, London, Canada
    Contribution
    Formal analysis, Investigation, Visualization, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
  2. Roland S Johansson

    Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
    Contribution
    Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Project administration
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3288-8326
  3. J Andrew Pruszynski

    Department of Physiology and Pharmacology, Western University, London, Canada
    Contribution
    Conceptualization, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing
    For correspondence
    andrew.pruszynski@uwo.ca
    Competing interests
    Reviewing editor, eLife
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0786-0081

Funding

Vetenskapsrådet (22209)

  • J Andrew Pruszynski

Canadian Institutes of Health Research (Foundation Grant)

  • J Andrew Pruszynski

Canada Research Chairs (Tier 2)

  • J Andrew Pruszynski

Health Research

  • J Andrew Pruszynski

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was funded by the Swedish Research Council (Project Grant 22209 to J.A.P.) and the Canadian Institutes of Health Research (Foundation Grant to J.A.P.). J.A.P received salary awards from the Human Frontiers Science Program (Long Term Fellowship) and the Canada Research Chairs program. We thank Ewa Jarocka, Carola Hjältén, Göran Westling, Anders Bäckström, Per Utsi and Etay Hay for their technical support.

Ethics

All participants provided written informed consent in accordance with the Declaration of Helsinki. The Umeå University ethics committee approved the study (PI: Johansson).

Senior Editor

  1. Tamar R Makin, University of Cambridge, United Kingdom

Reviewing Editor

  1. Hannes P Saal, University of Sheffield, United Kingdom

Reviewers

  1. Hannes P Saal, University of Sheffield, United Kingdom
  2. Benoit Delhaye, Université Catholique de Louvain, Belgium
  3. Saad S Nagi, Linköping University, Sweden

Publication history

  1. Preprint posted: June 3, 2022 (view preprint)
  2. Received: June 29, 2022
  3. Accepted: October 27, 2022
  4. Accepted Manuscript published: October 31, 2022 (version 1)
  5. Version of Record published: November 8, 2022 (version 2)

Copyright

© 2022, Sukumar et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 326
    Page views
  • 70
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Vaishnavi Sukumar
  2. Roland S Johansson
  3. J Andrew Pruszynski
(2022)
Precise and stable edge orientation signaling by human first-order tactile neurons
eLife 11:e81476.
https://doi.org/10.7554/eLife.81476

Further reading

    1. Neuroscience
    Kevin Vinberg, Jörgen Rosén ... Fredrik Ahs
    Research Article Updated

    Understanding the neural basis for individual differences in the skin conductance response (SCR) during discriminative fear conditioning may inform on our understanding of autonomic regulation in fear-related psychopathology. Previous region-of-interest (ROI) analyses have implicated the amygdala in regulating conditioned SCR, but whole brain analyses are lacking. This study examined correlations between individual differences in SCR during discriminative fear conditioning to social stimuli and neural activity throughout the brain, by using data from a large functional magnetic resonance imaging study of twins (N = 285 individuals). Results show that conditioned SCR correlates with activity in the dorsal anterior cingulate cortex/anterior midcingulate cortex, anterior insula, bilateral temporoparietal junction, right frontal operculum, bilateral dorsal premotor cortex, right superior parietal lobe, and midbrain. A ROI analysis additionally showed a positive correlation between amygdala activity and conditioned SCR in line with previous reports. We suggest that the observed whole brain correlates of SCR belong to a large-scale midcingulo-insular network related to salience detection and autonomic-interoceptive processing. Altered activity within this network may underlie individual differences in conditioned SCR and autonomic aspects of psychopathology.

    1. Neuroscience
    Rong Zhao, Stacy D Grunke ... Joanna L Jankowsky
    Research Article

    Neurodegenerative diseases are characterized by selective vulnerability of distinct cell populations; however, the cause for this specificity remains elusive. Here we show that entorhinal cortex layer 2 (EC2) neurons are unusually vulnerable to prolonged neuronal inactivity compared with neighboring regions of the temporal lobe, and that reelin+ stellate cells connecting EC with the hippocampus are preferentially susceptible within the EC2 population. We demonstrate that neuronal death after silencing can be elicited through multiple independent means of activity inhibition, and that preventing synaptic release, either alone or in combination with electrical shunting, is sufficient to elicit silencing-induced degeneration. Finally, we discovered that degeneration following synaptic silencing is governed by competition between active and inactive cells, which is a circuit refinement process traditionally thought to end early in postnatal life. Our data suggests that the developmental window for wholesale circuit plasticity may extend into adulthood for specific brain regions. We speculate that this sustained potential for remodeling by entorhinal neurons may support lifelong memory but renders them vulnerable to prolonged activity changes in disease.