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We recently reported the existence of fluctuations in neural signals that maypermit neurons to code multiple simultaneous stimuli sequentially across time.This required deploying a novel statistical approach to permit investigation ofneural activity at the scale of individual trials. Here we present tests usingsynthetic data to assess the sensitivity and specificity of this analysis. Wefabricated datasets to match each of several potential response patternsderived from single-stimulus response distributions. In particular, wesimulated dual stimulus trial spike counts that reflected fluctuating mixturesof the single stimulus spike counts, stable intermediate averages, singlestimulus winner-take-all, or response distributions that were outside the rangedefined by the single stimulus responses (such as summation or suppression). Wethen assessed how well the analysis recovered the correct response pattern as afunction of the number of simulated trials and the difference between thesimulated responses to each "stimulus" alone. We found excellent recovery ofthe mixture, intermediate, and outside categories (>97% percent correct), andgood recovery of the single/winner-take-all category (>90% correct) when thenumber of trials was >20 and the single-stimulus response rates were 50Hz and20Hz respectively. Both larger numbers of trials and greater separation betweenthe single stimulus firing rates improved categorization accuracy. Theseresults provide a benchmark, and guidelines for data collection, for use ofthis method to investigate coding of multiple items at the individual-trialtime scale.

Sensitivity and specificity of a Bayesian single trial analysis for time varying neural signals