Shifts in coding properties and maintenance of information transmission during adaptation in barrel cortex

Abstract

Neuronal responses to ongoing stimulation in many systems change over time, or "adapt." Despite the ubiquity of adaptation, its effects on the stimulus information carried by neurons are often unknown. Here we examine how adaptation affects sensory coding in barrel cortex. We used spike-triggered covariance analysis of single-neuron responses to continuous, rapidly varying vibrissa motion stimuli, recorded in anesthetized rats. Changes in stimulus statistics induced spike rate adaptation over hundreds of milliseconds. Vibrissa motion encoding changed with adaptation as follows. In every neuron that showed rate adaptation, the input-output tuning function scaled with the changes in stimulus distribution, allowing the neurons to maintain the quantity of information conveyed about stimulus features. A single neuron that did not show rate adaptation also lacked input-output rescaling and did not maintain information across changes in stimulus statistics. Therefore, in barrel cortex, rate adaptation occurs on a slow timescale relative to the features driving spikes and is associated with gain rescaling matched to the stimulus distribution. Our results suggest that adaptation enhances tactile representations in primary somatosensory cortex, where they could directly influence perceptual decisions.

Figure 1. Adaptive Responses to Stimuli with Switching Variance

(A) Vibrissa motion waveforms (central plot) were made by drawing instantaneous displacement values from a Gaussian distribution whose variance was switched every 5 s. The ratio of low to high variance was 0.49. The bottom plot shows the “mask” used to implement the switch in variance. Transitions were smoothed over 10 ms; stimulus values were smoothed over ∼5 ms and were white noise in frequency up to ∼210 Hz (see magnified plots at top; scale bars of magnified plots, vertical, 5 μm; horizontal, 25 ms). The inset plot shows the distributions' relative widths, where the x-axis is position and the y-axis is the probability density (scale bar, 20 μm). (B) A whisker displacement from x = A to x = B can occur against a context of high-variance (left) or low-variance stimuli (right). (C) Spike times from a single neuron's response over ten successive stimulus cycles. Stimulus values did not repeat, so the spikes were not temporally aligned on successive cycles.

Figure 2. Adaptive Modulation of Spike Rate

(A) Representation of stimulus distributions and switches during a cycle. (B and C) Behavior of the neuron shown in Figure 1B. Bin size, 100 ms. Dashed lines at 0 and 10 s show correspondence with start and end times of cycle. (B) Absolute spike rate averaged over switching cycles (1,080 repetitions). (C) Spike rates normalized by the total number of spikes in each 10-s cycle and then averaged over cycles, eliminating variations in absolute rate (spike count). Finally, the averaged rate was normalized by the spike rate standard deviation over the cycle, and its mean over the cycle was subtracted, giving a specific measurement of rate modulation. (D) Rate modulation in the normalized units of (C), pooled over all adapting single neurons (n = 9). Black, population average; gray, plus/minus population standard deviation. Rate modulation was robust and occurred over a similar timescale across the population. (E) Adaptation ratios. The firing rate at steady state (binned 4–5 s after each upwards switch in stimulus variance) was divided by the rate immediately after switching to high variance (binned 0–100 ms after the switch). Left: data points. Filled gray square, nonadapting neuron (n = 1); filled black circles, adapting single neurons (n = 13); open circles, adapting multineuron clusters (n = 20). Right: histogram of adaptation ratios for all recordings shown on the left side. Only a single cortical neuron showed nonadapting behavior. The asterisk denotes that this neuron's adaptation ratio was significantly different from that of the rest of the population (p < 0.001) (after Lilliefors test for normality on the rest of the population's distribution).

Figure 3. STC Analysis

Columns (i) and (ii) show results for two single neurons recorded in different animals. All results were computed for 2–5 s after stimulus switches. (A) STA and random averages normalized by the corresponding stimulus standard deviation. Cyan, random (prior distribution) average over low-variance periods; blue, STA over low-variance periods; magenta, random average over high-variance periods; red, STA over high-variance periods. Neuron (i) was typical in having an STA without significant structure; neuron (ii) had the largest STA among adapting neurons, although still small. Neither neuron showed adaptive changes in STA shape. (B) Covariance difference matrices, showing the difference between the spike-triggered and prior covariance matrices, for low- and high-variance periods, shown in units of the corresponding standard deviation (color bar). (C) Nonlinear input–output relationships for low-variance (blue) and high-variance (red) periods. Modulations of spike rate were normalized by average rate and plotted against the stimulus projection onto a significant feature extracted from the covariance difference matrix. Stimulus projections were plotted in absolute units proportional to real wafer displacement (equal in value to the low standard deviation). Error bars are the standard deviation from 30 repetitions of the estimation procedure. (D) Replotted input–output relationships with inputs normalized by their corresponding standard deviation. Error bars as in (C). For both neurons adaptation involved a rescaling of input–output relationships according to input range.

Figure 4. Firing Rate and STC of a Neuron That Lacked Adaptation

(A) Responses to ten successive stimulus cycles (bottom plot, variance mask). (B) Absolute rate modulation during switching cycles. Spiking rate remained high throughout high-variance periods and low throughout low-variance periods. Bin size, 100 ms; responses averaged over 1,040 repetitions. (C) Normalized rate modulation computed as for Figure 2B. (D–F) Covariance analysis computed 2–5 s after variance switches. (D) Spike-triggered and random averages. Cyan, random average, low-variance periods; blue, STA, low-variance periods; magenta, random average, high-variance periods; red, STA, high-variance periods. There was no change in STA shape. (E) Covariance difference matrices for low- and high-variance periods, normalized by the local standard deviation (color bar). There was very little structure in the covariance difference matrices, and no visible change after variance switches. (F) Nonlinear input–output functions plotted against the stimulus projection onto the STA measured in absolute units as in Figure 3C: low-variance (blue) and high-variance (red) periods. Error bars as in Figure 3C. The neuron was more sensitive during high-variance stimulation. (G) Nonlinear input–output functions normalized by the local standard deviation as in Figure 3D. Input–output relationships did not fully rescale.

Figure 5. Mutual Information per Spike between Responses and Stimulus Features during Adaptation

Black, average over adapting neurons (n = 8); green, nonadapting neuron from Figure 4. Error bars show standard error of mean. (A) Information conveyed about the most significant feature. During high-variance periods, absolute information was 0.12 ± 0.02 bits per spike; computed over adapting and nonadapting neurons, n = 9. (B) Estimate of summed information about all significant features. During high-variance periods, absolute summed information was 0.29 ± 0.07 bits per spike, n = 9. Neurons with adaptive responses transmitted constant quantities of information per spike in the face of changes in stimulus distribution, but for the neuron with no adaptation, mutual information decreased when the stimulus distribution changed.

Figure 6. Simulations of Adaptive Responses Using Fixed Features

(A) Stimulus segments were projected onto two orthogonal features (black): a sharper excitatory filter (top) and a longer suppressive filter (bottom). The projected stimulus was fed into a nonlinear function used to compute the probability of spiking at time t = 0. In red, filters recovered by STC analysis conducted on responses collected over a simulated 800 s. (B) Spike rate modulation upon onset of a square pulse stimulus (top) and upon an increase in white-noise stimulus variance (bottom). Responses averaged over 300 repetitions. Black and green curves show responses for simulations performed with different sets of parameters for the spiking nonlinearity. In all cases tested, adaptation to white noise was less pronounced than adaptation to the square pulse, but the timescale of adaptation was never longer than the duration of the longest (suppressive) filter. (C) Estimated normalized input–output functions from the STC analysis for low- (blue) and high-variance (red) periods: stimulus-dependent modulations of spike probability plotted as in Figure 3D. Note that the functions' zero-crossing level did change. This is expected because the way in which firing probability is modulated by the stimulus projection onto the feature of interest will differ across high- and low-variance periods, even for a nonadapting model. (D) Estimated normalized input–output functions represented as in (C), for stimuli divided by their distributions' standard deviations. The true function appears in black.