• DocumentCode
    947992
  • Title

    A Biologically Inspired Spiking Neural Network for Sound Source Lateralization

  • Author

    Voutsas, Kyriakos ; Adamy, Jürgen

  • Author_Institution
    Darmstadt Univ. of Technol., Darmstadt
  • Volume
    18
  • Issue
    6
  • fYear
    2007
  • Firstpage
    1785
  • Lastpage
    1799
  • Abstract
    In this paper, a binaural sound source lateralization spiking neural network (NN) will be presented which is inspired by most recent neurophysiological studies on the role of certain nuclei in the superior olivary complex (SOC) and the inferior colliculus (IC). The binaural sound source lateralization neural network (BiSoLaNN) is a spiking NN based on neural mechanisms, utilizing complex neural models, and attempting to simulate certain parts of nuclei of the auditory system in detail. The BiSoLaNN utilizes both excitatory and inhibitory ipsilateral and contralateral influences arrayed in only one delay line originating in the contralateral side to achieve a sharp azimuthal localization. It will be shown that the proposed model can be used both for purposes of understanding the mechanisms of an NN of the auditory system and for sound source lateralization tasks in technical applications, e.g., its use with the Darmstadt robotic head (DRH).
  • Keywords
    acoustic signal processing; hearing; neural nets; BiSoLaNN biologically inspired spiking NN; auditory system; binaural sound source lateralization neural network; neurophysiological studies; sound signal processing; sound source lateralization; Brain-like systems; computational neuroscience; neural network (NN); neuronal modeling; sound source lateralization; spiking neuron; Action Potentials; Animals; Auditory Pathways; Brain Stem; Computer Simulation; Evoked Potentials, Auditory, Brain Stem; Feedback; Functional Laterality; Hearing; Humans; Inferior Colliculi; Models, Neurological; Neural Inhibition; Neural Networks (Computer); Neurons; Olivary Nucleus; Pitch Discrimination; Sound; Sound Localization; Synaptic Transmission;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/TNN.2007.899623
  • Filename
    4359176