Signal processing of odor inputs to the olfactory bulb (OB) changes through top-down modulation whose shaping of neural rhythms in response to changes in stimulus intensity is not understood. Here we asked whether the representation of a high vs. low intensity odorant in the OB by oscillatory neural activity changed as the animal learned to discriminate odorant concentration ranges in a go-no go task. We trained mice to discriminate between high vs. low concentration odorants by learning to lick to the rewarded group (low or high). We recorded the local field potential (LFP) in the OB of these mice and calculated the theta-referenced beta or gamma oscillation power (theta phase-referenced power, or tPRP). We found that as the mouse learned to differentiate odorant concentrations, tPRP diverged between trials for the rewarded vs. the unrewarded concentration range. For the proficient animal, linear discriminant analysis was able to predict the rewarded odorant group and the performance of this classifier correlated with the percent correct behavior in the odor concentration discrimination task. Interestingly, the behavioral response and decoding accuracy were asymmetric as a function of concentration when the rewarded stimulus was shifted between the high and low odorant concentration ranges. A model for decision making motivated by the statistics of OB activity that uses a single threshold in a logarithmic concentration scale displays this asymmetry. Taken together with previous studies on the intensity criteria for decisions on odorant concentrations, our finding suggests that OB oscillatory events facilitate decision making to classify concentrations using a single intensity criterion.

嗅球（OB）气味输入的信号处理通过自上而下的调制而变化，其响应刺激强度变化的神经节律的形成尚不清楚。在这里，我们询问当动物在“走-不走”任务中学会区分气味剂浓度范围时，振荡神经活动对产科中高强度和低强度气味剂的表征是否会发生变化。我们通过学习舔奖励组（低或高）来训练小鼠区分高浓度和低浓度的气味剂。我们记录了这些小鼠 OB 中的局部场电位 (LFP)，并计算了 θ 参考 β 或 γ 振荡功率（θ 相位参考功率，或 tPRP）。我们发现，当小鼠学会区分气味浓度时，奖励浓度范围和无奖励浓度范围的试验之间的 tPRP 会出现差异。对于熟练的动物，线性判别分析能够预测奖励的气味组，并且该分类器的性能与气味浓度辨别任务中正确行为的百分比相关。有趣的是，当奖励刺激在高和低气味浓度范围之间转移时，行为反应和解码准确性作为浓度的函数是不对称的。一个由 OB 活动统计数据驱动的决策模型显示了这种不对称性，该模型使用对数浓度标度中的单个阈值。结合之前关于气味剂浓度决策的强度标准的研究，我们的发现表明 OB 振荡事件有助于使用单一强度标准对浓度进行分类的决策。