Global Tactile Coding in Rat Barrel Cortex in the Absence of Local Cues

Although whisker-related perception is based predominantly on local, near-instantaneous coding, global, intensive coding, which integrates the vibrotactile signal over time, has also been shown to play a role given appropriate behavioral conditions. Here, we study global coding in isolation by studying head-fixed rats that identified pulsatile stimuli differing in pulse frequency but not in pulse waveforms, thus abolishing perception based on local coding. We quantified time locking and spike counts as likely variables underpinning the 2 coding schemes. Both neurometric variables contained substantial stimulus information, carried even by spikes of single barrel cortex neurons. To elucidate which type of information is actually used by the rats, we systematically compared psychometric with neurometric sensitivity based on the 2 coding schemes. Neurometric performance was calculated by using a population-encoding model incorporating the properties of our recorded neuron sample. We found that sensitivity calculated from spike counts sampled over long periods (>1 s) matched the performance of rats better than the one carried by spikes time-locked to the stimulus. We conclude that spike counts are more relevant to tactile perception when instantaneous kinematic parameters are not available

Beyond GLMs: A Generative Mixture Modeling Approach to Neural System Identification

<div><p><i>Generalized linear models</i> (GLMs) represent a popular choice for the probabilistic characterization of neural spike responses. While GLMs are attractive for their computational tractability, they also impose strong assumptions and thus only allow for a limited range of stimulus-response relationships to be discovered. Alternative approaches exist that make only very weak assumptions but scale poorly to high-dimensional stimulus spaces. Here we seek an approach which can gracefully interpolate between the two extremes. We extend two frequently used special cases of the GLM—a linear and a quadratic model—by assuming that the spike-triggered and non-spike-triggered distributions can be adequately represented using Gaussian mixtures. Because we derive the model from a generative perspective, its components are easy to interpret as they correspond to, for example, the spike-triggered distribution and the interspike interval distribution. The model is able to capture complex dependencies on high-dimensional stimuli with far fewer parameters than other approaches such as histogram-based methods. The added flexibility comes at the cost of a non-concave log-likelihood. We show that in practice this does not have to be an issue and the mixture-based model is able to outperform generalized linear and quadratic models.</p></div

Performance as a function of available data.

<p>The factored STM was trained with different random subsets of the training trials and evaluated on all test trials for one SA cell (<b>A</b>) and one RA cell (<b>B</b>). The horizontal axis represents the number of spikes in the training set. Shown are the average performances (solid blue line) along with 90% confidence intervals (5th and 95th percentile). For comparison, we also show the performance of the linear model trained with different subsets of the data, the average performances of the non-factored STM, and the quadratic model trained on the entire training set. Note that the factored STM outperforms the generalized linear model even when only a small fraction of the dataset is used.</p

Quantitative model comparison.

<p>Linear, quadratic and spike-triggered mixture models (STM) were evaluated on 8 slowly adapting cells (<b>A</b>) and 10 rapidly adapting cells (<b>B</b>). The performance of each model is measured in terms of the cross-entropy (negative log-likelihood) averaged over all cells (smaller is better). Light bars correspond to models which ignore the spike history, dark bars correspond to models which explicitly take the spike history into account. By subtracting the cross-entropy from the estimated entropy of the spike trains (“Prior”), an estimate of mutual information (MI) between stimuli and spike trains is obtained. The bars in <b>C</b> and <b>D</b> show (from left to right) the differences in performance between the linear model and the prior, the quadratic model and the linear model, and the STM and the quadratic model (with and without spike history dependency, respectively). The two right most bars show the improvement of the PSTH over the STM with and without spike history dependency.</p

Spike trains generated by real and model neurons.

<p>Stimuli corresponding to the spike trains are shown at the top. The first row below the stimulus shows spike trains and interspike interval distributions generated by one <i>slowly adapting</i> (<b>A</b>) and one <i>rapidly adapting</i> cell (<b>B</b>) of the rat's whisker system. The two cells shown are the SA cell and RA cell where the quantitative improvement in performance gained by using an STM over a quadratic model was largest.</p

Illustration of the spike-triggered mixture model (STM).

<p><b>A</b>. A sigmoidal nonlinearity is applied to a log-likelihood ratio of two mixtures of Gaussians to determine the firing rate of the model, which is then used to generate spikes. <b>B</b>. By making a naive Bayes assumption, additional information and measurements such as interspike interval distributions can easily be incorporated into the model in the form of additional log-likelihood ratios.</p

Trigeminal ganglion cells recorded under white noise stimulus of rat

<p>.MAT files containing white noise stimuli used to drive rats whiskers.</p> <p>.MAT files containing the digitized binary spike trains of trigeminal ganglion neurons that responded to the previously mentioned whisker stimulation (Classified as slowly adapting neurons).</p> <p>More details about this dataset can be found at:</p> <p>http://www.frontiersin.org/neural_circuits/10.3389/fncir.2013.00190/abstract</p> <p>If you use this dataset for any purposes, please cite the above mentioned publication.</p

Trigeminal ganglion cells recorded under white noise stimulus of rat

<p>.MAT files containing white noise stimuli used to drive rats whiskers.</p> <p>.MAT files containing the digitized binary spike trains of trigeminal ganglion neurons (Classified as rapidly adapting cells) that responded to the previously mentioned whisker stimulation.</p> <p>More details about this dataset can be found at:</p> <p>http://www.frontiersin.org/neural_circuits/10.3389/fncir.2013.00190/abstract</p> <p>If you use this dataset for any purposes, please cite the above mentioned publication.</p> <p> </p