Robust temporal coding in the trigeminal system

Abstract

The ability of rats to use their whiskers for fine tactile discrimination rivals that of humans using their fingertips. Rats perform discriminations rapidly and accurately while palpating the environment with their whiskers. This suggests that whisker deflections produce a robust and reliable neural code. Whisker primary afferents respond with highly reproducible temporal spike patterns to transient stimuli. Here we show that, with the use of a linear kernel, any of these reproducible response trains recorded from an individual neuron can reliably predict complex whisker deflections. These predictions are significantly improved by integrating responses from neurons with opposite angular preferences.

Fig. 1

Single spike trains accurately predict complex stimuli. (A) Individual whiskers were stimulated 10 mm from their base by a piezoelectric device with white noise stimuli (10 to 25 Hz, 10 to 125 Hz, or 10 to 625 Hz) applied in each neuron's preferred direction. Upward deflections are in the preferred direction; downward deflections, opposite direction. Variability in stimulus waveforms was less than 5%. (B) Peristimulus rasters (top) and histograms (bottom) recorded from a trigeminal ganglion neuron in response to 50 presentations of each stimulus. Spikes were sampled at 10 kHz and sorted. msec, milliseconds. (C) Predictions (red) of actual stimulus (black) position, velocity, and acceleration at the three frequencies tested. (D) Individual spike trains used to compute the stimulus predictions in (C). (E) Kernels calculated for each of the stimulus parameters predicted in (C), with cross-correlation values between the original and predicted waveforms below the curves. Pos, position; Vel, velocity; Acc, acceleration.

Fig. 2

Shifting spike times degrades stimulus predictions. (A) The jittering process. Top raster is the original spike train in response to the 125 Hz stimulus, and bottom raster is the jittered train created by randomly shifting each spike from a Gaussian distribution of ±10 ms. (B) Prediction values degrade as jitter is increased. For clarity, only predictions of stimulus velocity are depicted. Correlation coefficients of the predictions were normalized to their unjittered value, and group data are plotted (mean ± SD) for all neurons (n = 16). Predictions degrade more rapidly for higher stimulus frequencies.

Fig. 3

Prediction values significantly improve when integrating spikes elicited in response to stimulation in opposite directions. (A) Original 125-Hz stimulus (black), single predictions (green), and integrated predictions (red). (B) Integrated spike train used to compute the predictions in (A). Spikes in response to the original stimulus were assigned the value of +1; those in response to the reversed stimulus were assigned the value of -1. (C) Kernels used to compute the predictions in (A) and corresponding correlation coefficients: single (green) and integrated (red). (D) Group data (mean ± SD) for all neurons (n = 16). Prediction values increase for all stimulus parameters at all frequencies tested. (E) Integration significantly increases predictions for every neuron. Prediction values (125-Hz position) computed for single (S) and integrated (I) spike trains.