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Titlebook: Biologically Inspired Signal Processing for Chemical Sensing; Agustín Gutiérrez,Santiago Marco Book 2009 Springer-Verlag Berlin Heidelberg

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https://doi.org/10.1007/978-3-319-58694-6ysteine and tyrosine, from its differential-pulse voltammetric signal. WNN generalization ability was validated with training processes of k-fold cross validation with random selection of the testing set.
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Biologically Inspired Signal Processing for Chemical Sensing
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“Sloppy Engineering” and the Olfactory System of Insectsetworks in the past. In recent work we have analyzed the olfactory pathway of insects as a pattern classification device with unstructured (random) connectivity. In this chapter I will review these and related results and discuss the implications for applications in artificial olfaction. As we will
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From ANN to Biomimetic Information Processingted to be quite different from it. ANN typically lack scalability and mostly rely on supervised learning, both of which are biologically implausible features. Here we describe and evaluate a novel cortex-inspired hybrid algorithm. It is found to perform on par with a Support Vector Machine (SVM) in
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