Elementary motion sequence detectors in whisker somatosensory cortex

Abstract

How the somatosensory cortex (S1) encodes complex patterns of touch, such as those that occur during tactile exploration, is poorly understood. In the mouse whisker S1, temporally dense stimulation of local whisker pairs revealed that most neurons are not classical single-whisker feature detectors, but instead are strongly tuned to two-whisker sequences that involve the columnar whisker (CW) and one specific surround whisker (SW), usually in a SW-leading-CW order. Tuning was spatiotemporally precise and diverse across cells, generating a rate code for local motion vectors defined by SW-CW combinations. Spatially asymmetric, sublinear suppression for suboptimal combinations and near-linearity for preferred combinations sharpened combination tuning relative to linearly predicted tuning. This resembles computation of motion direction selectivity in vision. SW-tuned neurons, misplaced in the classical whisker map, had the strongest combination tuning. Thus, each S1 column contains a rate code for local motion sequences involving the CW, thus providing a basis for higher-order feature extraction.

Figure 1.

S1 neurons preferentially encode CW-SW sequences. (a) Whisker stimuli applied via a 3×3 whisker array centered on the recorded S1 column (here, D1). Bottom: Whisker stimuli for Experiment 1. (b) Top, Mean single-whisker tuning across all units by layer; shading is SEM (L2/3 = 36, L4 = 48, L5 = 44 units). Bottom, Spiking response (z-scored within each unit) to CW deflection vs. best SW deflection for each unit. Histogram shows distribution of CW-tuned and SW-tuned units. (c) Tuning curves for 2-whisker sequences for 2 example units. Yellow and blue bars show the response to CW-SW or SW-SW combinations; left and right sectors show negative Δt (SW precedes CW) or positive Δt (CW precedes SW). Each bar shows the peak response across Δt’s for that whisker combination. Black bars show single-whisker responses. (d) Response of each unit to its best 2- whisker sequence vs. best single whisker. (e) Fraction of units whose best sequence contained the CW (n=89 CW-tuned and 53 SW-tuned units). Error bar is SE of the sample proportion. (f) Spiking response (z-scored within each unit) to best CW-SW vs. best SW-SW sequence for each unit. (g) Tuning sharpness, quantified as lifetime sparseness, among different sets of whisker sequences. Within each set, sequences were ranked from strongest to weakest response, and lifetime sparseness was calculated for increasing number of sequences ranked. Thus, tuning sharpness can be compared between the N best CW-SW and N best SW-SW sequences (at X=N on the x-axis). Shaded regions are SEM.

Figure 2.

Dense spatiotemporal mapping of CW-SW sequences reveals combination tuning in S1. (a) Stimulus set for Experiment 2. Whisker combinations were delivered at Δt distributed between ±50ms at 1 ms intervals. Top right, example CW-SW stimulus waveforms at +10ms Δt. (b) Example unit recorded in D1 column to correlated CW-SW stimuli. Each raster plot shows spiking across all Δt’s for each combination, with the D2-D1 combination enlarged below. The Δt tuning curve was calculated by averaging spikes in the spike count window for each Δt and smoothing (see Methods). (c) Columnar-whisker spatiotemporal receptive field (cwSTRF) for the unit in b. CSI (tuning sharpness across whisker combinations) was calculated for each Δt. (d) Combination tuning curves, rasters and PSTHs for 4 example combination-selective units, calculated within ± 5ms of each unit’s best Δt. Asterisk, best combination. The top-left unit is the same as in (b) and (c). (e) Distribution of CSI for significantly combination selective units (shuffle test, α ≤ .05, FDR control). (f) Average combination tuning for all combination-selective units (n = 187), and for the 25% most- and least-selective combination-selective units. Tuning curves were aligned by their peak response (asterisk). Gray, tuning after spike counts were shuffled across combinations. Green, spontaneous firing rate. Shaded regions are SEM. (g) Distribution of best CW-SW combination identity was non-uniform (Rayleigh test, p=4e-19; n = 451 units). Each bin is one CW-SW combination, organized by SW position on the face (D=dorsal, V=ventral, R=rostral, C=caudal).

Figure 3.

Nonlinear sharpening of combination tuning enhances rate coding for whisker combinations. (a) Comparison of measured and linearly predicted CSI at best Δt. (b) Distributions of measured and linearly predicted CSI for combination-selective (n=187) and non-selective units (n=264). Inset show mean CSI±SEM, 2 sample t-test, p<0.005. (c-d) Average peak-aligned combination tuning across all combination-selective units (c), or the 25% most-selective and 25% least-selective combination-selective units (d). Asterisk indicates peak response. Shading is SEM. Sample sizes as in 2f. Black, mean response to the CW deflected individually. (e) Average performance of a neural population decoder that predicts CW-SW combination identity based on single-trial spiking activity of combination selective units. The decoding model for each ensemble size was fit by randomly sampling (with replacement) 1000 times from recorded units. (f) Mean confusion matrix of neural decoder with 187 combination-selective units. Entries along the diagonal are percent correct classifications for each CW-SW combination and rows sum to 1. Orange and gray bars are % correct classification for measured and linear responses respectively. (g) Average performance of a neural population decoder performing a binary discrimination between best and suboptimal CW-SW combinations. Stimuli for training and testing were either at each neuron’s best Δt, or in specific Δt ranges. Shaded regions and error bars in (e-g) are SD across 2500 bootstrap decoding trials.

Figure 4.

SW-tuned units show strong rate coding for whisker combinations. (a) Probability distributions of combination-selectivity indices for CW (n = 169) and SW-tuned units (n = 282). Insets show mean CSI±SEM, *** 2-sample t-test, p=3.4e-4. (b) Mean normalized CW-SW combination tuning for these same CW and SW-tuned units. Tuning curves were aligned at the best combination. Asterisk indicates peak response. Shaded regions are SEM. (c) Average performance of neural decoders based on either CW- or SW-tuned units in predicting CW-SW combination identity. Decoders were built as in Fig. 3e. Shaded regions are SD across 2500 bootstrap trials. (d) Confusion matrix for the decoder trained on SW-tuned units only. Plotted as in Fig. 3f. Red and blue bars denote mean for SW and CW-tuned units respectively. Error bars are SD across 2500 bootstrap trials

Figure 5.

Best combination responses at best Δt are enhanced relative to a global sublinear scaling. (a) Comparison of measured and linearly predicted combination responses at each unit’s best Δt. Black line is the line of best fit with the y-intercept set to 0. (n = 3,608 combinations, with 7 suboptimal and 1 optimal combination per unit). (b) Left, comparison between linearity (measured response divided by the linear prediction) and z-scored CW-SW combination response. Right, distribution of linearity indices for best and suboptimal combinations. Dashed lines are the mean linearity indices. (c) Average linearity indices (measured response divided by the linear prediction) for best and suboptimal combinations at best Δt for measured and shuffled data. The peak linearity index across all Δt’s is also shown for suboptimal stimuli. *** p < 0.001, 2-sample t-test. Panels (b) and (c) quantify the individual data points in (a).

Figure 6.

Linear and nonlinear computations underlying combination tuning. (a) Schematic showing global sublinear scaling (0.64x normalization), followed by combination-specific enhancement or suppression, that represent linear and nonlinear components for synthesis of combination tuning. See text for details. The polar tuning curve for an example combination-selective unit summarizes these steps (linear summation->scaling->combination-specific enhancement or suppression ->measured response). (b) Mean rank-ordered combination tuning curves for combination-selective (n = 187) and non-selective units (n = 264). Bottom plots show measured tuning, linear prediction, and the 0.64x-scaled linear prediction. Color legend is the same as (a). Top, combination-specific nonlinearity, defined as residual between measured response and 0.64x-scaled linear prediction. Unpaired t-test, * p=0.001 and p=0.002. (c) Mean combination-specific enhancement or suppression as a function of ranked strength of SW single-whisker response for combination-selective (n = 187) and non-selective units (n = 264). Error bars in (b-c) are SEM.

Figure 7.

Δt tuning and preference for tactile sequences inbound to the CW. (a) Mean Δt tuning curves for combination-selective units (n = 187) aligned at best Δt. All Δt tuning curves were normalized to each unit’s peak response. Shaded regions are SEM. (b) Mean z-scored Δt tuning curves for best (n = 187) and suboptimal (n = 1309) CW-SW combinations. Z-scoring was done separately for each unit. (c) Cumulative variance explained by the principal components (PCs) of z-scored Δt tuning curves. (d) First three PCs. (e) Distribution of weights (or score) for PC 1 across best and suboptimal combinations, within combination-selective and non-selective units. Plots for PC 2 and 3 do not separate suboptimal stimuli by unit type. (f) CSI and alignment indices for combination-selective units that had a negative (n = 121) or positive best Δt (n = 56). Open circles are individual units, red line is mean and shaded region is SEM. The alignment index was the Pearson correlation coefficient between linearly predicted combination responses and combination-specific facilitation or suppression. 2-sample t-test, * p=0.03 (left) and p=0.01 (right). (g) Decoding of combination identity (as in Fig. 4) across different ranges of Δt. Shaded region is SD across 1000 bootstrap trials.

Figure 8.

Combination-selectivity measured in awake S1. (a) Behavioral trial structure. 1- and 2-whisker stimuli are densely interleaved in each trial. On ~40% of trials, mice must detect a brief (2-ms) visual stimulus, to ensure alertness. (b) Behavioral performance. Mice lick in the response window after visual stimuli, but much less in no-visual stimulus trials. Error bars are SE of sample proportion. (c) Mean ranked whisker receptive field for CW-tuned units (n = 46) in awake mice (Experiment 3) vs. anesthetized (n = 89) (Experiment 1). 2-sample t-test, ** p < .001. Shaded regions are SEM across units. Inset, average single-whisker receptive field across all whisker-responsive single units (n=74) in awake mice. (d) Evoked response to best sequence vs. best single whisker in awake mice. Slope of line = 1.24. (e) CW-SW combination tuning for 3 example combination-selective units, compared to linear predicted tuning. Shaded regions are SEM across trials. (f) Mean peak-aligned CW-SW tuning curve across all combination-selective units (n=29), after normalizing to peak response in each unit. This is compared to linear predicted tuning or shuffled data representing no tuning. (g) Measured vs predicted linear response magnitude for each CW-SW combination at each unit’s best Δt. Shaded regions are SEM (h) Linearity index for best (n = 74) and suboptimal CW-SW combinations (n = 518) in (g). Shaded regions are SEM. 2-sample t-test, ***p=6.2e-17. (i) CW-SW tuning is sharper than linear predicted tuning for most units. (j) Peak CW-SW response at each Δt for each unit (n = 74). Responses were z-scored within each unit across all CW-SW combinations and Δt’s. Shading is SEM. (k) Decoding of best vs suboptimal CW-SW combinations using the binary decoder, for ensembles of combination-selective units. Chance performance is 50%. Line and shading are mean and SEM across 2500 bootstrap trials.