Instituto de Fisiología Biología Molecular y Neurociencias (IFIByNE-UBA-CONICET) and Departamento de Fisiología, Biología Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina.
Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina.
PLoS Comput Biol. 2023 Jun 21;19(6):e1011176. doi: 10.1371/journal.pcbi.1011176. eCollection 2023 Jun.
The broad receptive field of the olfactory receptors constitutes the basis of a combinatorial code that allows animals to detect and discriminate many more odorants than the actual number of receptor types that they express. One drawback is that high odor concentrations recruit lower affinity receptors which can lead to the perception of qualitatively different odors. Here we addressed the contribution that signal-processing in the antennal lobe makes to reduce concentration dependence in odor representation. By means of calcium imaging and pharmacological approach we describe the contribution that GABA receptors play in terms of the amplitude and temporal profiles of the signals that convey odor information from the antennal lobes to higher brain centers. We found that GABA reduces the amplitude of odor elicited signals and the number of glomeruli that are recruited in an odor-concentration-dependent manner. Blocking GABA receptors decreases the correlation among glomerular activity patterns elicited by different concentrations of the same odor. In addition, we built a realistic mathematical model of the antennal lobe that was used to test the viability of the proposed mechanisms and to evaluate the processing properties of the AL network under conditions that cannot be achieved in physiology experiments. Interestingly, even though based on a rather simple topology and cell interactions solely mediated by GABAergic lateral inhibitions, the AL model reproduced key features of the AL response upon different odor concentrations and provides plausible solutions for concentration invariant recognition of odors by artificial sensors.
嗅觉受体的广泛感受野构成了组合编码的基础,使动物能够检测和区分比它们表达的实际受体类型更多的气味。一个缺点是,高浓度的气味会招募亲和力较低的受体,这可能导致对不同性质的气味的感知。在这里,我们研究了触角叶中的信号处理在减少气味表示的浓度依赖性方面的作用。通过钙成像和药理学方法,我们描述了 GABA 受体在从触角叶到大脑高级中枢传递气味信息的信号幅度和时间特性方面的作用。我们发现 GABA 以浓度依赖的方式降低了气味诱发信号的幅度和被招募的嗅球数量。阻断 GABA 受体可降低相同气味的不同浓度诱发的嗅球活动模式之间的相关性。此外,我们构建了一个真实的触角叶数学模型,用于测试所提出的机制的可行性,并在生理学实验中无法实现的条件下评估 AL 网络的处理特性。有趣的是,尽管基于相当简单的拓扑结构和仅由 GABA 能侧抑制介导的细胞相互作用,AL 模型复制了不同气味浓度下 AL 反应的关键特征,并为人工传感器对气味的浓度不变识别提供了合理的解决方案。