Role of spike timing in the forelimb somatosensory cortex of the rat

Abstract

The aim of this study was to test the hypothesis that the significance of spike timing in somatosensory processing is not a specific feature of the whisker cortex but a more general characteristic of the primary somatosensory cortex. We recorded ensembles of neurons using microwire arrays implanted in the deep layers of the forelimb region of the rat primary somatosensory cortex in response to step stimuli delivered to the cutaneous surface of the contralateral body. We used a recently developed peristimulus time histogram (PSTH)-based classification method to investigate the temporal precision of the code by evaluating how changing the bin size (from 40 to 1 msec) would affect the ability of the ensemble responses to discriminate stimulus location on a single-trial basis. The information related to the discrimination was redundantly distributed within the ensembles, and the ability to discriminate stimulus location increased when decreasing the bin size, reaching a maximum at 4 msec. In our experiment, at 4 msec bin size the first spike per neuron after the stimulus conveyed almost as much information as the entire responses, so the temporal precision of the code was preserved in the first spikes. Subsequent spikes were less frequent but conveyed more information per spike. Finally, not only the trials correctly classified but also the trials incorrectly classified conveyed information about stimulus location with a similar temporal precision. We conclude that the role of spike timing in cortical somatosensory processing is not an exclusive feature of the highly specialized rat trigeminal system, but a more general property of the rat primary somatosensory cortex.

Figure 1.

Experimental procedures. A, Drawing of stimulus locations. One hundred punctuate step stimuli were delivered to each of 10 locations on both sides of the body. Namely, digit 3, digit 4, dorsal paw (dorsum), ankle, forearm, and arm were stimulated dorsally, where as digit 1, digit 2, digit 5, and ventral paw (palm) were stimulated ventrally. B, C, Example of spike sorting from an electrode (electrode 19) where three neurons (b, c, d) were discriminated. Note that electrode 19 indicates the third electrode of the 16 channel array implanted in the right cortex in a bilateral implant on one animal; electrodes 1-16 indicate the array implanted in the left cortex. B, The three cells had distinguishable spike waveforms. Thick lines represent the average wave forms (sig019b, 949 spikes; sig019c, 1980 spikes; sig019d, 1073 spikes). Thin lines are representative waveforms of single spikes (10 waveforms per neuron). C, The autocorrelograms show the expected refractory period and different patterns for the three cells. For details about the relationship between the refractory period and the autocorrelogram, see Bar-Gad et al. (2001). D, Histological confirmation of electrode placement. The photomicrograph shows an example of postmortem brain slice Nissl stained after a stimulating current had been placed down the electrodes at the end of the experiments. The black arrows indicate the location of two electrode tips in the deep layers (V-VI) of the forepaw somatosensory cortex.

Figure 2.

Visual interpretation of the PSTH-based classification method. The “neural images” (top) represent the average responses (i.e., the PSTHs) of 38 forepaw cortical neurons to four different locations. Each column in a neural image corresponds to the PSTH of one neuron in a 40-msec-long poststimulus time window from +5 to +44 msec after stimulus onset. The window is divided into 40 bins of 1 msec. Colors are light-graded from minimal response (black) to maximal response (white). In the classification, a single trial in the testing set (bottom) is “aligned” over all the neural images (as indicated by the arrows) and assigned to the “closest” neural image in the sense of a minimum pixel-by-pixel mean squared error. In the example, the squared error between the single trial and the neural images is, from left to right, 7.11, 7.41, 7.37, and 7.23. The single trial is therefore classified to the top left neural image, as indicated by the gray arrow.

Figure 3.

Single-trial classification of stimulus location using ensembles of neurons. A, Scatter plot of the classification performance (expressed as percentage of trials correctly classified and averaged across all bin sizes) as a function the number of neurons in the ensemble. The solid line is the best linear fit of the data, and the dotted line indicates chance classification performance (10%). The performance of the classification increases as the number of neurons increases. B, Comparison of classification performance evaluated by the percentage of trial correctly classified (y-axis) and the bits of mutual information between neural responses and stimuli (x-axis). Each point represents the performance of one ensemble at a given bin size (so there are 10 × 7 = 70 points). The two measures of performance are well correlated. C, Neuron dropping. Classification performance is plotted as a function of the neurons included in the analysis. For each number of neurons N on the x-axis, the value on the y-axis represents the bits of information at 4 msec bin size averaged over all the populations with at least N neurons. Gray points correspond to single variables, and black squares (every 10 variables) correspond to the different neurons. The maximum number of neurons used was 28. The smooth and sublinear performance increase when adding neurons reveals information redundancy within the ensembles in this simple task (Franco et al., 2004) and supports the idea of distributed processing in the somatosensory cortex (Nicolelis et al., 1998; Ghazanfar et al., 2000).

Figure 4.

Role of spike timing in the somatosensory code. A, B, Classification performance as a function of the bin size. The performance represents the percentage of trials correctly classified (A) and the bits of mutual information between stimuli and neural responses (evaluated from the confusion matrix) (B) averaged across 10 ensembles of neurons from five rats. The gray stars represent significant differences. The largest bin size, 40 msec, provides the poorest classification performance.

Figure 5.

Error analysis. A, Drawing of the “mislocalizations” of trials incorrectly classified. Arrows from location a to location b indicate that at least 15% (solid lines) or 13% (dotted lines) of the trials incorrectly classified when a was contacted were assigned to b. The mislocalization was somatotopically organized, in the sense that the chosen location was usually a skin site nearby the correct location. The 24 arrows represent 47.9% of the trials incorrectly classified, compared with 26.7% (i.e., 24 of 90) as predicted by chance. B, Probability distribution function of the error distance, D, of the trials not correctly classified (D >1), including all bin sizes and all neuron ensembles. The correct location is the second choice in ∼30% of the trials incorrectly classified, the third guess in ∼20% of the trials incorrectly classified, and so on. This highly asymmetric distribution shows that even the trials incorrectly classified contain information about stimulus location. C, The percentage of trials incorrectly classified for which the correct location was the second choice (i.e., D = 2) is shown as a function of the bin size. Values represent averages across 10 ensembles of neurons. The gray star indicates significant differences. As for the trials correctly classified, the poorest performance (minimal percentage of trials incorrectly classified with D = 2) is obtained at 40 msec bin size.