• DocumentCode
    1917130
  • Title

    Using temporal binding for connectionist recruitment learning over delayed lines

  • Author

    Gunay, C. ; Maida, Anthony S.

  • Author_Institution
    Center for Adv. Comput. Studies, Louisiana Univ., Lafayette, LA, USA
  • Volume
    1
  • fYear
    2003
  • fDate
    20-24 July 2003
  • Firstpage
    224
  • Abstract
    The temporal correlation hypothesis proposes using distributed synchrony for the binding stimulus features. However, synchronized spikes must travel over cortical circuits that have varying-length pathways, leading to mismatched arrival times. This raises the question of how initial stimulus-dependent synchrony might be preserved at a destination binding site. Earlier, we proposed constraints on tolerance and segregation parameters for a phase-coding approach, within cortical circuits, to address this question [C. Gunay et. al., July 2001] The purpose of the present paper is twofold. First, we conduct simulation experiments to test the proposed constraints. Second, we explore the practicality of temporal binding to drive a process of long-term memory formation based on a recruitment learning method [J.A. Feldman, 1982]. A network based on Valiant´s neuroidal architecture [Leslie G. Valiant, 1994] is used to demonstrate the coalition between temporal binding and recruitment. Complementing similar approaches, we implement a continuous-time learning procedure allowing computation with spiking neurons. Our results indicate that when tolerance and segregation parameters obey our proposed constraints, the assemblies of correct bindings are dominant over assemblies of spurious bindings in reasonable operating conditions.
  • Keywords
    biocybernetics; cognitive systems; continuous time systems; correlation theory; learning (artificial intelligence); neural nets; Valiant neuroidal architecture; connectionist recruitment learning; continuous-time learning procedure; cortical circuits; delayed lines; distributed synchrony; long-term memory formation; phase segregation; phase-coding approach; recruitment learning algorithm; segregation parameter; spiking neurons; stimulus dependent synchrony; temporal binding; temporal correlation hypothesis; tolerance parameter; tolerance window; varying-length pathways; Assembly; Biological system modeling; Circuit simulation; Circuit testing; Cognitive science; Computer architecture; Delay lines; Learning systems; Neurons; Recruitment;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2003. Proceedings of the International Joint Conference on
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7898-9
  • Type

    conf

  • DOI
    10.1109/IJCNN.2003.1223348
  • Filename
    1223348