Cleland Thomas A, Borthakur Ayon
Computational Physiology Laboratory, Department of Psychology, Cornell University, Ithaca, NY, United States.
Computational Physiology Laboratory, Field of Computational Biology, Cornell University, Ithaca, NY, United States.
Front Comput Neurosci. 2020 Sep 23;14:579143. doi: 10.3389/fncom.2020.579143. eCollection 2020.
We describe an integrated theory of olfactory systems operation that incorporates experimental findings across scales, stages, and methods of analysis into a common framework. In particular, we consider the multiple stages of olfactory signal processing as a collective system, in which each stage samples selectively from its antecedents. We propose that, following the signal conditioning operations of the nasal epithelium and glomerular-layer circuitry, the plastic external plexiform layer of the olfactory bulb effects a process of category learning-the basis for extracting meaningful, quasi-discrete representations from the metric space of undifferentiated olfactory quality. Moreover, this early categorization process also resolves the foundational problem of how odors of interest can be recognized in the presence of strong competitive interference from simultaneously encountered background odorants. This problem is fundamentally constraining on early-stage olfactory encoding strategies and must be resolved if these strategies and their underlying mechanisms are to be understood. Multiscale general theories of olfactory systems operation are essential in order to leverage the analytical advantages of engineered approaches together with our expanding capacity to interrogate biological systems.
我们描述了一种嗅觉系统运作的综合理论，该理论将跨尺度、阶段和分析方法的实验结果纳入一个通用框架。具体而言，我们将嗅觉信号处理的多个阶段视为一个集合系统，其中每个阶段都从其前序阶段进行选择性采样。我们提出，在鼻上皮和肾小球层回路的信号调节操作之后，嗅球的可塑性外丛状层实现了一种类别学习过程——这是从未分化的嗅觉质量度量空间中提取有意义的、准离散表征的基础。此外，这种早期分类过程还解决了一个基本问题，即在同时遇到的背景气味剂产生强烈竞争干扰的情况下，如何识别感兴趣的气味。这个问题从根本上限制了早期嗅觉编码策略，如果要理解这些策略及其潜在机制，就必须解决这个问题。嗅觉系统运作的多尺度通用理论对于利用工程方法的分析优势以及我们不断扩大的探究生物系统的能力至关重要。
