05, χ2 test), and using both the first and second sniff cycles resulted in only a small increase in accuracy (Figure 5D). Therefore, spike counts in ensembles of aPC neurons appear to be sufficient to explain both the speed and accuracy of decisions in an odor mixture discrimination task. If firing rates across ensembles of aPC neurons are used by the brain to form behavioral responses, and if sensory uncertainty reduces performance accuracy, as in the mixture trials, then we might be able to observe signaling pathway trial-by-trial correlations between decoding based on these neural representations and the animals’ choices. To test this idea, we first compared neuronal firing

rates on correct and error choices for a given stimulus, a measure

analogous to “choice probability,” a measure that has been used previously to test the role of a neural representation in behavior (Britten et al., 1996; Cury and Uchida, 2010; Parker and Newsome, 1998). We found a low average correlation between the firing rates of individual neurons and subjects’ choices (avg. choice prob. = 0.51 ± 0.011; Figures 5E and 5F). This correlation was somewhat smaller than those found in previous observations in visual cortex (0.53–0.7; Britten et al., 1996; Cohen and Newsome, 2009; Dodd et al., 2001; Uka and DeAngelis, 2004). However, if the information for choices is distributed across a large number of uncorrelated aPC neurons such that the contribution of single neurons is diluted (Cohen and Newsome, 2009), then we reasoned that the accuracy of decoding based on simultaneously recorded ensembles may be correlated on a trial-by-trial basis with behavioral INCB018424 mw choices. Indeed, we found that

patterns of spike counts across aPC neurons in correct trials provided significantly higher decoding accuracy than patterns in error next trials (Figure 5G; p = 0.030, Wilcoxon test). In contrast, decoding using peak timing or latency did not show a significant difference between correct and error trials (Figures 5H and 5I; p > 0.05, Wilcoxon test). Therefore, spike rates in aPC not only carry substantial stimulus information, they are also correlated at an ensemble level with the behavioral choices of the animal. The above results indicate that odor information is coded by a large number of neurons in aPC. A critical feature of information coding in neuronal ensembles is the structure and magnitude of correlated fluctuations in firing, which can affect the ability of downstream neurons to decode the information. A simple example of ensemble decoding is population averaging or pooling. By this strategy, neuronal noise can, in principle, be eliminated by averaging the activity of a large number of neurons. However, if noise is not random across neurons, that is, if neural activity cofluctuates across neurons, the benefit of pooling can be significantly curtailed (Cohen and Kohn, 2011; Zohary et al., 1994).