Neural mechanisms and functional requirements of complex odour identification in the mammalian olfactory system

大脑必须解决的一项基本计算任务是对高维感觉数据进行分析和分类。我们认为，嗅觉系统，作为最古老的感觉系统，已经发展了一个规范的解决方案，这个问题。因此，本研究的目的是探讨嗅觉模型系统中感觉数据的皮层处理过程。该项目的目标不仅是使用生物学上合理的数据驱动网络模型来分析和识别这种处理背后的神经机制，而且还使用机器学习算法来量化嗅觉系统的分类性能。（一个嗅球和一个嗅皮层）会融合，为了真实地再现嗅觉信息处理的两个最重要的结构，并探索嗅觉中的振荡活动，系统这是特别感兴趣的，因为振荡（特别是在θ和γ波段）被认为是至关重要的皮质信息处理。嗅觉皮层模型的模拟表明，振荡活动的起源在于嗅球内，而传统上，振荡皮层活动归因于嗅觉皮层本身内的复杂相互作用。此外，以前的模拟还表明，嗅觉皮层的结构分离成不同的子网络，这也与传统观点相反，传统观点认为嗅觉系统是一个自联想记忆网络。因此，本计画将集中于嗅觉系统的这两个结构-功能假说的严格的、以模型为基础的测试。最后，本计画将解决嗅觉系统分类效能的更抽象的问题。该项目将探索嗅觉皮层内信息的表征和处理是否能改善复杂气味的分类。为此，国家的最先进的分类算法（即支持向量机）将被用来分类复杂的气味从模拟神经活动的嗅球或模拟神经活动的嗅觉皮层。将比较这两种分类，以了解嗅觉皮质中的信息处理是否会导致分类增强。同样，这将被测试的嗅觉系统的结构功能假说。

A fundamental computational task the brain has to solve is the analysis and classification of high-dimensional sensory data. We suggest that the olfactory system, as the oldest sensory system, has evolved a canonical solution to this problem. Therefore, the goal of the project proposed here, is to investigate the cortical processing of sensory data in the model system olfaction. The project aims to not only analyse and identify the neuronal mechanisms underlying this processing, using a biologically plausible, data-driven network model, but, in addition, to quantify the classification performance of the olfactory system using machine learning algorithms.In the proposed project two existing neuronal network models (one of the olfactory bulb and one of the olfactory cortex) will be fused, in order to realistically represent the two most important structures of olfactory information processing and to explore the oscillatory activity in the olfactory system. This is of particular interest since oscillations (especially in the theta and gamma band) are thought to be crucial in cortical information processing. Simulations of the model of olfactory cortex have suggested that the origin of oscillatory activity lies within the olfactory bulb, whereas, traditionally, oscillatory cortical activity is ascribed to complex interactions within the olfactory cortex itself. Furthermore, previous simulations also suggest a structural segregation of the olfactory cortex into distinct subnetworks, which also stands in contrast to the traditional view, which sees the olfactory system as an autoassociative memory network. Therefore, the proposed project will focus on a rigorous, model-based test of these two structure-function hypotheses of the olfactory system.Finally, the project will address the more abstract question of classification performance in the olfactory system. The project will explore whether the representation and processing of information within the olfactory cortex improves the classification of complex odours. To this end, state-of-the-art classification alogrithms (i.e. support vector machines) will be used to classify complex odours from either simulated neural activity in the olfactory bulb or simulated neural activity from the olfactory cortex. These two classifications will be compared in order to see whether information processing in the olfactory cortex leads to enhanced classification. Again, this will be tested for both structure-function hypotheses of the olfactory system.