Convergence across tactile afferent types in primary and secondary somatosensory cortices

Abstract

Integration of information by convergence of inputs onto sensory cortical neurons is a requisite for processing higher-order stimulus features. Convergence across defined peripheral input classes has generally been thought to occur at levels beyond the primary sensory cortex, however recent work has shown that this does not hold for the convergence of slowly-adapting and rapidly-adapting inputs in primary somatosensory cortex. We have used a new analysis method for multi-unit recordings, to show convergence of inputs deriving from the rapidly-adapting and Pacinian channels in a proportion of neurons in both primary and secondary somatosensory cortex in the anaesthetised cat. We have validated this method using single-unit recordings. The secondary somatosensory cortex has a greater proportion of sites that show convergence of this type than primary somatosensory cortex. These findings support the hypothesis that the more complex features processed in higher cortical areas require a greater degree of convergence across input classes, but also shows that this convergence is apparent in the primary somatosensory cortex.

Figure 1. Example stimulus and recording.

(A) Photo of anterior parietal cortex with outlines of sulci superimposed. The planar array was inserted into the paw representation region of S1 (black square). A linear array was inserted into S2 region located in the suprasylvian sulci (yellow rectangle). (B) Average baseline-subtracted spike rate for multi-unit activity (MUA) recorded from planar array to the stimulus condition 160 µm at 20 Hz and 16 µm at 200 Hz. Stimulus site is digit 4 of contralateral fore paw. Each of the 100 squares represents the activity on an electrode of the 10×10 planar array. (C) Raster plot of MUA from a single electrode from the planar array for 50 repetitions of stimulus conditions shown in B. (D) Profile of the complex stimulus: 20 Hz + 200 Hz sinusoid — superimposed on a step indentation — aligned with raster plot.

Figure 2. Multi-unit responses and classifications.

Each 3D bar graph represents the MUA at one electrode when stimulated with the combinatory 20 Hz + 200 Hz sinusoids. The x-y axes represent the amplitude of the component sinusoids, and the z-axis is the spike rate averaged over the repetitions of the given stimulus condition. The graphs are colour-coded according to their classification: RA (A and D), PC (B and E), RA-PC linear interaction (F), RA-PC facilitative interaction (C). The top row (A, B, and C) are recordings from S1 while the bottom row (D, E and F) are recordings from S2.

Figure 3. RA-PC linear and facilitative interactions.

Average spike rate of multi-unit activity from individual channels exhibiting RA-PC linear interaction or RA-PC facilitative interaction. The stimulus conditions plotted are pure 20 Hz sinusoids (grey square), pure 200 Hz sinusoids (grey circle), and the simultaneous combination of 20 Hz and 200 Hz (black triangle). The response to the combined stimulus is compared to the baseline-subtracted summed response from the pure 20 Hz and pure 200 Hz stimulus (black dotted line). Error bars denote standard deviation.

Figure 4. Proportions of RA-PC response classes.

(A) The distribution of channels in RA-like, PC-like, RA-PC Linear Interaction and RA-PC Facilitative Interaction classes found using the linear arrays in S1 (left) and S2 (right) across all responsive channels and stimulus sites. (B) The distribution of classes found using the planar array in S1 (top left), the averaged baseline-subtracted activity recorded by the planar array from one animal (bottom left), and the spatial organization of these classes (bottom right) for this given recording. White represents unresponsive channels.

Figure 5. Single-unit responses and classifications.

Each 3D bar graph represents the SUA for the spike shown in each corresponding inset when stimulated with the combinatory 20 Hz + 200 Hz sinusoids. The x-y axes represent the amplitude of the component sinusoids, and the z-axis is the spike rate averaged over the repetitions of the given stimulus condition. The graphs are colour-coded according to their classification: RA (A and E), PC (B and F), RA-PC linear interaction (C and G), RA-PC facilitative interaction (D and H). The left examples (A, B, C and D) are recordings from S1 while the examples on the right (E, F and G) are recordings from S2.

Figure 6. Proportions of RA-PC response classes in single-unit data.

The distribution of isolated single-units in RA-like, PC-like, RA-PC Linear Interaction and RA-PC Facilitative Interaction classes found in S1 (top) and S2 (bottom) across stimulus sites.