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Processing of chemical sensor arrays with a biologically inspired model of olfactory coding

机译:用嗅觉生物学启发模型处理化学传感器阵列

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This paper presents a computational model for chemical sensor arrays inspired by the first two stages in the olfactory pathway: distributed coding with olfactory receptor neurons and chemotopic convergence onto glomerular units. We propose a monotonic concentration-response model that maps conventional sensor-array inputs into a distributed activation pattern across a large population of neuroreceptors. Projection onto glomerular units in the olfactory bulb is then simulated with a self-organizing model of chemotopic convergence. The pattern recognition performance of the model is characterized using a database of odor patterns from an array of temperature modulated chemical sensors. The chemotopic code achieved by the proposed model is shown to improve the signal-to-noise ratio available at the sensor inputs while being consistent with results from neurobiology.
机译:本文提出了一种化学传感器阵列的计算模型,该模型受嗅觉途径的前两个阶段的启发:嗅觉受体神经元的分布式编码以及在肾小球单位上的趋化性会聚。我们提出了一种单调的浓度响应模型,该模型将常规的传感器阵列输入映射到跨大量神经受体的分布激活模式。然后用化学趋同的自组织模型模拟嗅球中的肾小球单位的投影。使用来自温度调制化学传感器阵列的气味模式数据库来表征模型的模式识别性能。所提出的模型实现的化学编码显示出可改善传感器输入处可用的信噪比,同时与神经生物学的结果一致。

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