Sensory and Behavioural Neuroscience Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan.
Elife. 2022 Mar 7;11:e76882. doi: 10.7554/eLife.76882.
Sensory systems are often tasked to analyse complex signals from the environment, separating relevant from irrelevant parts. This process of decomposing signals is challenging when a mixture of signals does not equal the sum of its parts, leading to an unpredictable corruption of signal patterns. In olfaction, nonlinear summation is prevalent at various stages of sensory processing. Here, we investigate how the olfactory system deals with binary mixtures of odours under different brain states by two-photon imaging of olfactory bulb (OB) output neurons. Unlike previous studies using anaesthetised animals, we found that mixture summation is more linear in the early phase of evoked responses in awake, head-fixed mice performing an odour detection task, due to dampened responses. Despite smaller and more variable responses, decoding analyses indicated that the data from behaving mice was well discriminable. Curiously, the time course of decoding accuracy did not correlate strictly with the linearity of summation. Further, a comparison with naïve mice indicated that learning to accurately perform the mixture detection task is not accompanied by more linear mixture summation. Finally, using a simulation, we demonstrate that, while saturating sublinearity tends to degrade the discriminability, the extent of the impairment may depend on other factors, including pattern decorrelation. Altogether, our results demonstrate that the mixture representation in the primary olfactory area is state-dependent, but the analytical perception may not strictly correlate with linearity in summation.
感觉系统通常需要分析来自环境的复杂信号，将相关部分与不相关部分区分开来。当信号混合物不等于其各部分的总和时，信号的这种分解过程极具挑战性，从而导致信号模式不可预测的损坏。在嗅觉中，非线性加和普遍存在于感觉处理的各个阶段。在这里，我们通过双光子成像研究了嗅觉球（OB）输出神经元，在不同的大脑状态下，嗅觉系统如何处理气味的二元混合物。与使用麻醉动物的先前研究不同，我们发现，在清醒、头部固定的老鼠执行气味检测任务时，由于反应减弱，在诱发反应的早期阶段，混合物加和更具有线性。尽管反应更小且更具可变性，但解码分析表明，来自行为老鼠的数据具有良好的可辨别性。奇怪的是，解码准确性的时间进程与加和的线性度没有严格相关。此外，与未受训练的老鼠进行比较表明，学习准确执行混合物检测任务并不伴随着更线性的混合物加和。最后，我们使用模拟演示了，虽然饱和的次线性往往会降低可辨别性，但损害的程度可能取决于其他因素，包括模式去相关。总之，我们的结果表明，初级嗅觉区域的混合物表示是状态依赖性的，但分析感知可能与加和的线性度没有严格的相关性。
