We create a spiking neural network of Integrate and Fire neurons with spike frequency adaption based on parameters adjusted for our e-nose device, and investigate the use of this model for odor classification. Addition of spike frequency adaptation term brings the model closer to the response of the olfactory system. Data from Cyranose 320, a polymer based 32-sensor array, is used to test the system and create unique dynamic spiking patterns. The results for four analytes are presented.


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    Titel :

    Spiking neural networks' model with spike frequency adaptation for e-nose


    Beteiligte:
    Badiei, S. (Autor:in) / Abdel-Aty-Zohdy, H. (Autor:in)


    Erscheinungsdatum :

    01.07.2011


    Format / Umfang :

    1524928 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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