By Thomas Nowotny (auth.), Agustín Gutiérrez, Santiago Marco (eds.)
This quantity provides a set of study advances in biologically encouraged sign processing for chemical sensing. The olfactory process, and the gustatory procedure to a minor quantity, has been taken within the final a long time as a resource of notion to strengthen synthetic sensing structures. The functionality of this organic process outperforms in lots of facets that in their synthetic counterpart. hence, the aim of researchers during this box is to appreciate and catch these positive aspects that make the olfactory approach particularly fitted to the processing of chemical info. the popularity of odors via the olfactory approach involves a couple of sign processing features reminiscent of preprocessing, dimensionality relief, distinction enhancement, and class. utilizing mathematical versions to imitate the structure of the olfactory procedure, those processing capabilities may be utilized to chemical sensor signs. This ebook offers a few historical past at the olfactory method together with a assessment on info processing within the insect olfactory approach besides a proposed sign processing structure in line with the mammalian cortex. It additionally offers a few bio-inspired methods to method chemical sensor indications akin to an olfactory mucosa to enhance smell separation and a version of olfactory receptor neuron convergence to correlate sensor responses to an scent and his organoleptic homes.
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Extra info for Biologically Inspired Signal Processing for Chemical Sensing
The effectiveness of the insect antenna/nose has been determined by using radiolabeled bombykol, counting nerve impulses generated by the receptor neuron, and measuring the behavioural response of the male moth. At the behavioural threshold the neuronal signal/noise discrimination works at the theoretical limit. 1 Introduction For a low olfactory threshold several sensory functions need to be optimized. Odour molecules have to be a) effectively caught by the antenna from the air space, and b) conducted with little loss to the olfactory receptor neurons.
As can be seen, the new algorithm with on-line learning has a much superior drift tolerance under these conditions. 2 Classification of MNIST Data The algorithm was run on the entire MNIST data set. Of the 10,000 images in the test set, 95% were correctly classified (Johansson 2006). When only a feedforward configuration of BCPNN was used, with no intermediate layer generated by the hybrid algorithm, 84% of the images were correctly classified. Note that the learning in this case is not gradient descent but one-shot and correlation based.
Note that some patches comprise more than one subfield. (b) Example of the specific receptive field of one of the 10 units in the patch marked with orange (with two subfields). We can illustrate each step in the previous section by applying the proposed method to a real classification task. In our example, the bit depth of all MNIST images is lowered by reducing the number of gray levels to eight. One input hypercolumn, corresponding to one image pixel, then can take on eight different values. The general dependences between the image pixels are calculated by the mutual information.