Whisker motion sensation in rat vibrissa system: from neuron to behavior

A fundamental aim of systems neuroscience is to identify how sensory stimuli are represented in neuronal activity, and how the activity of sensory neurons is “read out” by downstream neuronal structures and contribute to behaviour. The rat whisker-barrel system is structurally well-studied and represents the major channel through which rodents collect information about their surrounding environment. We used rat whisker touch as a model system to investigate the effect of adaptation on sensory coding efficiency and the correlation between the response function of cortical neurons and behaviour of rats in a sensory detectiondiscrimination paradigm. We quantified the response characteristics of individual neurons as well as cortical populations under three adaptation states in an anaesthetize preparation. Adaptation caused a systematic rightward shift in the neuronal response functions. Stimulus presentation reduced single neuron trial-to-trial response variability (captured by Fano factor) and correlations in the population response variability (noise correlation). Noise correlations were positive, and thus detrimental to coding efficiency (captured by the mutual information between neuronal responses and stimuli). Adaptation increased the total information despite increasing the noise correlation between neurons. We further compared the performance of multiple decoding approaches when population responses were simply pooled together, or when their responses were integrated after applying an optimum set of weights. Optimum weights were found by the analytical solution that maximized the average signal to noise ratio based on the Fisher Linear Discriminant analysis. A biologically plausible decoder that integrates neuronal activity after applying different synaptic weights provided an optimal read out of the sensory signal. In this decoding scheme, the less informative neurons could still provide information about the network state, and thus improve decoding efficiency. To link the neuronal activity to rat sensation, we trained rats to make discriminations between sinusoidal vibrations simultaneously presented to the left and right whiskers. Consistent with neuronal response functions, the rats were most sensitive to changes in the mean speed of vibration, the same physical feature that was best represented in neuronal activity. Furthermore, the behavioural performances showed a nonlinear profile which was remarkably similar to that of the neuronal populations

Dynamics of population activity in rat sensory cortex: Network correlations predict anatomical arrangement and information content

To study the spatiotemporal dynamics of neural activity in a cortical population, we implanted a 10 × 10 microelectrode array in the vibrissal cortex of urethane-anesthetized rats. We recorded spontaneous neuronal activity as well as activity evoked in response to sustained and brief sensory stimulation. To quantify the temporal dynamics of activity, we computed the probability distribution function (PDF) of spiking on one electrode given the observation of a spike on another. The spike-triggered PDFs quantified the strength, temporal delay, and temporal precision of correlated activity across electrodes. Nearby cells showed higher levels of correlation at short delays, whereas distant cells showed lower levels of correlation, which tended to occur at longer delays. We found that functional space built based on the strength of pairwise correlations predicted the anatomical arrangement of electrodes. Moreover, the correlation profile of electrode pairs during spontaneous activity predicted the “signal” and “noise” correlations during sensory stimulation. Finally, mutual information analyses revealed that neurons with stronger correlations to the network during spontaneous activity, conveyed higher information about the sensory stimuli in their evoked response. Given the 400-μm-distance between adjacent electrodes, our functional quantifications unravel the spatiotemporal dynamics of activity among nearby and distant cortical columns

Informational basis of sensory adaptation: Entropy and single-spike efficiency in rat barrel cortex

We showed recently that exposure to whisker vibrations enhances coding efficiency in rat barrel cortex despite increasing correlations in variability (Adibi et al., 2013). Here, to understand how adaptation achieves this improvement in sensory representation, we decomposed the stimulus information carried in neuronal population activity into its fundamental components in the framework of information theory. In the context of sensory coding, these components are the entropy of the responses across the entire stimulus set (response entropy) and the entropy of the responses conditional on the stimulus (conditional response entropy). We found that adaptation decreased response entropy and conditional response entropy at both the level of single neurons and the pooled activity of neuronal populations. However, the net effect of adaptation was to increase the mutual information because the drop in the conditional entropy outweighed the drop in the response entropy. The information transmitted by a single spike also increased under adaptation. As population size increased, the information content of individual spikes declined but the relative improvement attributable to adaptation was maintained

Correlation between cortical state and locus coeruleus activity: Implications for sensory coding in rat barrel cortex

Cortical state modulates the background activity of cortical neurons, and their evoked response to sensory stimulation. Multiple mechanisms are involved in switching between cortical states including various neuromodulatory systems. Locus Coeruleus (LC) is one of the major neuromodulatory nuclei in the brainstem with widespread projections throughout the brain and modulates the activity of cells and networks. Here, we quantified the link between the LC spontaneous activity, cortical state and sensory processing in the rat vibrissal somatosensory “barrel” cortex (BC). We simultaneously recorded unit activity from LC and BC along with prefrontal electroencephalogram (EEG) while presenting brief whisker deflections under urethane anesthesia. The ratio of low to high frequency components of EEG (referred to as the L/H ratio) was employed to identify cortical state. We found that the spontaneous activity of LC units exhibited a negative correlation with the L/H ratio. Cross-correlation analysis revealed that changes in LC firing preceded changes in the cortical state: the correlation of the LC firing profile with the L/H ratio was maximal at an average lag of −1.2 s. We further quantified BC neuronal responses to whisker stimulation during the synchronized and desynchronized states. In the desynchronized state, BC neurons showed lower stimulus detection threshold, higher response fidelity, and shorter response latency. The most prominent change was observed in the late phase of BC evoked activity (100–400 ms post stimulus onset): almost every BC unit exhibited a greater late response during the desynchronized state. Categorization of the BC evoked responses based on LC activity (into high and low LC discharge rates) resulted in highly similar response profiles compared to categorization based on the cortical state (low and high L/H ratios). These findings provide evidence for the involvement of the LC neuromodulatory system in desynchronization of cortical state and the consequent enhancement of sensory coding efficiency

Sampling time and performance in rat whisker sensory system

We designed a behavioural paradigm for vibro-tactile detection to characterise the sampling time and performance in the rat whisker sensory system. Rats initiated a trial by nose-poking into an aperture where their whiskers came into contact with two meshes. A continuous nose-poke for a random duration triggered stimulus presentation. Stimuli were a sequence of discrete Gaussian deflections of the mesh that increased in amplitude over time - across 5 conditions, time to maximum amplitude varied from 0.5 to 8 seconds. Rats indicated the detected stimulus by choosing between two reward spouts. Two rats completed more than 500 trials per condition. Rats' stimulus sampling duration increased and performance dropped with increasing task difficulty. For all conditions the median reaction time was longer for correct trials than incorrect trials. Higher rates of increment in stimulus amplitude resulted in faster rise in performance as a function of stimulus sampling duration. Rats' behaviour indicated a dynamic stimulus sampling whereby nose-poke was maintained until a stimulus was correctly identified or the rat experienced a false alarm. The perception was then manifested in behaviour after a motor delay. We thus modelled the results with 3 parameters: signal detection, false alarm, and motor delay. The model captured the main features of the data and produced parameter estimates that were biologically plausible and highly similar across the two rats

Population decoding in rat barrel cortex: optimizing the linear readout of correlated population responses

Sensory information is encoded in the response of neuronal populations. How might this information be decoded by downstream neurons? Here we analyzed the responses of simultaneously recorded barrel cortex neurons to sinusoidal vibrations of varying amplitudes preceded by three adapting stimuli of 0, 6 and 12 µm in amplitude. Using the framework of signal detection theory, we quantified the performance of a linear decoder which sums the responses of neurons after applying an optimum set of weights. Optimum weights were found by the analytical solution that maximized the average signal-to-noise ratio based on Fisher linear discriminant analysis. This provided a biologically plausible decoder that took into account the neuronal variability, covariability, and signal correlations. The optimal decoder achieved consistent improvement in discrimination performance over simple pooling. Decorrelating neuronal responses by trial shuffling revealed that, unlike pooling, the performance of the optimal decoder was minimally affected by noise correlation. In the non-adapted state, noise correlation enhanced the performance of the optimal decoder for some populations. Under adaptation, however, noise correlation always degraded the performance of the optimal decoder. Nonetheless, sensory adaptation improved the performance of the optimal decoder mainly by increasing signal correlation more than noise correlation. Adaptation induced little systematic change in the relative direction of signal and noise. Thus, a decoder which was optimized under the non-adapted state generalized well across states of adaptation