Lateral inhibitory networks: Synchrony, edge enhancement, and noise reduction

C. Glackin, L. Maguire, L. McDaid, J. Wade

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    3 Citations (Scopus)


    This paper investigates how layers of spiking neurons can be connected using lateral inhibition in different ways to bring about synchrony, reduce noise, and extract or enhance features. To illustrate the effects of the various
    connectivity regimes spectro-temporal speech data in the form
    of isolated digits is employed. The speech samples are preprocessed
    using the Lyon’s Passive Ear cochlear model, and then encoded into tonotopically arranged spike arrays using the BSA spiker algorithm. The spike arrays are then subjected to various lateral inhibitory connectivity regimes configured
    by two connectivity parameters, namely connection length and
    neighbourhood size. The combination of these parameters are
    demonstrated to produce various effects such as transient
    synchrony, reduction of noisy spikes, and sharpening of spectrotemporal
    Original languageEnglish
    Title of host publicationProcs of the 2011 International Joint Conference on Neural Networks
    Subtitle of host publication(IJCNN)
    PublisherInstitute of Electrical and Electronics Engineers (IEEE)
    Number of pages7
    ISBN (Print)978-1-4244-9637-2
    Publication statusPublished - 2011
    Event2011 Int Joint Conf on Neural Networks IJCNN - San Jose, United States
    Duration: 31 Jul 20115 Aug 2011


    Conference2011 Int Joint Conf on Neural Networks IJCNN
    Country/TerritoryUnited States
    CitySan Jose


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