A major challenge of sensory systems neuroscience is to quantify brain activity underlying perceptual experiences and to explain this activity as the outcome of elemental neuronal response properties. Rats make extremely fine discriminations of texture by “whisking” their vibrissae across an object's surface, yet the neuronal coding underlying texture sensations remains unknown. Measuring whisker vibrations during active whisking across surfaces, we found that each texture results in a unique “kinetic signature” defined by the temporal profile of whisker velocity. We presented these texture-induced vibrations as stimuli while recording responses of first-order sensory neurons and neurons in the whisker area of cerebral cortex. Each texture is encoded by a distinctive, temporally precise firing pattern. To look for the neuronal coding properties that give rise to texture-specific firing patterns, we delivered horizontal and vertical whisker movements that varied randomly in time (“white noise”) and found that the response probabilities of first-order neurons and cortical neurons vary systematically according to whisker speed and direction. We applied the velocity-tuned spike probabilities derived from white noise to the sequence of velocity features in the texture to construct a simulated texture response. The close match between the simulated and real responses indicates that texture coding originates in the selectivity of neurons to elemental kinetic events.

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.

Rats and mice palpate objects with their whiskers to generate tactile sensations. This form of active sensing endows the animals with the capacity for fast and accurate texture discrimination. The present work is aimed at understanding the nature of the underlying cortical signals. We recorded neuronal activity from barrel cortex while rats used their whiskers to discriminate between rough and smooth textures. On whisker contact with either texture, firing rate increased by a factor of two to ten. Average firing rate was significantly higher for rough than for smooth textures, and we therefore propose firing rate as the fundamental coding mechanism. The rat, however, cannot take an average across trials, but must make an immediate decision using the signals generated on each trial. To estimate single-trial signals, we calculated the mutual information between stimulus and firing rate in the time window leading to the rat's observed choice. Activity during the last 75 ms before choice transmitted the most informative signal; in this window, neuronal clusters carried, on average, 0.03 bits of information about the stimulus on trials in which the rat's behavioral response was correct. To understand how cortical activity guides behavior, we examined responses in incorrect trials and found that, in contrast to correct trials, neuronal firing rate was higher for smooth than for rough textures. Analysis of high-speed films suggested that the inappropriate signal on incorrect trials was due, at least in part, to nonoptimal whisker contact. In conclusion, these data suggest that barrel cortex firing rate on each trial leads directly to the animal's judgment of texture.

Rats achieve remarkable texture discriminations by sweeping their facial whiskers along surfaces. This work explores how neurons at two levels of the sensory pathway, trigeminal ganglion and barrel cortex, carry information about such stimuli. We identified two biologically plausible coding mechanisms, spike counts and patterns, and used "mutual information" to quantify how reliably neurons in anesthetized rats reported texture when "decoded" according to these candidate mechanisms. For discriminations between surfaces of different coarseness, spike counts could be decoded reliably and rapidly (within 30 ms after stimulus onset in cortex). Information increased as responses were considered as spike patterns with progressively finer temporal precision. At highest temporal resolution (spike sequences across six bins of 4 ms), the quantity of "information" in patterns rose 150% for ganglion neurons and 110% for cortical neurons above that in spike counts. In some cases, patterns permitted discriminations not supported by spike counts alone.

Rats, using their whiskers, have excellent capabilities in texture discrimination. What is the representation of texture in rat somatosensory cortex? We hypothesize that as rats "whisk" over a surface, the spatial frequency of a grooved or pebbled texture is converted to a temporal frequency of whisker vibration. Surface features such as groove depth or grain size modulate the amplitude of this vibration. Validation of the hypothesis depends on showing that vibration parameters have distinct neuronal representations in cortex. To test this, we delivered sinusoidal vibrations to the whisker shaft and analyzed cortical neuronal activity. Seven amplitudes and seven frequencies were combined to construct 49 stimuli while recording activity through a 10 x 10 microelectrode array inserted into the middle layers of barrel cortex. We find that cortical neurons do not explicitly encode vibration frequency (f) or amplitude (A) by any coding measure (average spike counts over different time windows, spike timing patterns in the peristimulus time histograms or in autocorrelograms). Instead, neurons explicitly encode the product of frequency and amplitude, which is proportional to the mean speed of the vibration. The quantity Af is an invariant because neuronal response encodes this feature independently of the values of the individual terms A and f. This was true across a wide time scale of firing rate measurements, from 5 to 500 msec. We conclude that vibration kinetics are rapidly and reliably encoded in the firing rate of cortical ensembles. Therefore, the cortical representation of vibration speed could underlie texture discrimination.

Understanding the operations of neural networks in the brain requires an understanding of whether interactions among neurons can be described by a pairwise interaction model, or whether a higher order interaction model is needed. In this article we consider the rate of synchronous discharge of a local population of neurons, a macroscopic index of the activation of the neural network that can be measured experimentally. We analyse a model based on physics' maximum entropy principle that evaluates whether the probability of synchronous discharge can be described by interactions up to any given order. When compared with real neural population activity obtained from the rat somatosensory cortex, the model shows that interactions of at least order three or four are necessary to explain the data. We use Shannon information to compute the impact of high-order correlations on the amount of somatosensory information transmitted by the rate of synchronous discharge, and we find that correlations of higher order progressively decrease the information available through the neural population. These results are compatible with the hypothesis that high-order interactions play a role in shaping the dynamics of neural networks, and that they should be taken into account when computing the representational capacity of neural populations.

Rats can make extremely fine texture discriminations by "whisking" their vibrissa across the surface of an object. We have investigated one hypothesis for the neuronal basis of texture representation by measuring how clusters of neurons in the barrel cortex of anesthetized rats encode the kinetic features of sinusoidal whisker vibrations. Mutual information analyses of spike counts led to a number of findings. Information about vibration kinetics became available as early as 6 msec after stimulus onset and reached a peak at ϳ20 -30 msec. Vibration speed, proportional to the product of vibration amplitude (A) and frequency ( f), was the kinetic property most reliably reported by cortical neurons. Indeed, by measuring information when the complete stimulus set was collapsed into feature-defined groups, we found that neurons reduced the dimensionality of the stimulus from two features (A, f) to a single feature, the product Af. Moreover, because different neurons encode stimuli in the same manner, information loss was negligible even when the activity of separate neuronal clusters was pooled. This suggests a decoding scheme whereby target neurons could capture all available information simply by summating the signals from separate barrel cortex neurons. These results indicate that neuronal population activity provides sufficient information to allow nearly perfect discrimination of two vibrations, based on their deflection speeds, within a time scale comparable with that of a single whisking motion across a surface.

Exposure of cortical cells to sustained sensory stimuli results in changes in the neuronal response function. This phenomenon, known as adaptation, is a common feature across sensory modalities. Here, we quantified the functional effect of adaptation on the ensemble activity of cortical neurons in the rat whisker-barrel system. A multishank array of electrodes was used to allow simultaneous sampling of neuronal activity. We characterized the response of neurons to sinusoidal whisker vibrations of varying amplitude in three states of adaptation. The adaptors produced a systematic rightward shift in the neuronal response function. Consistently, mutual information revealed that peak discrimination performance was not aligned to the adaptor but to test amplitudes 3-9 m higher. Stimulus presentation reduced single neuron trial-to-trial response variability (captured by Fano factor) and correlations in the population response variability (noise correlation). We found that these two types of variability were inversely proportional to the average firing rate regardless of the adaptation state. Adaptation transferred the neuronal operating regime to lower rates with higher Fano factor and noise correlations. Noise correlations were positive and in the direction of signal, and thus detrimental to coding efficiency. Interestingly, across all population sizes, the net effect of adaptation was to increase the total information despite increasing the noise correlation between neurons.