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

点击翻译按钮获取中文摘要

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.