• DocumentCode
    2209797
  • Title

    Effects of neural entrainment within a biologically realistic network

  • Author

    Canfield, John ; Carter, Michael

  • Author_Institution
    Dept. of Electr. & Comput. Eng., New Hampshire Univ., Durham, NH, USA
  • fYear
    1997
  • fDate
    21-22 May 1997
  • Firstpage
    17
  • Lastpage
    18
  • Abstract
    A network of realistic neuron models interconnected by Hebbian synapses is shown to be capable of autoassociative recall. However, a collective network property, the robust entrainment of activity between neurons, leads to several interesting network level phenomena not seen in attractor networks with conventional neuron models. These include phase-locked bursting patterns of neural activity, the temporal segmentation of mixed pattern input cues, and the functionality to serve as a subunit for the storage of temporal sequences. Given the previously established accuracy of the utilized single neuron model as a reduced order approximation of the Hodgkin-Huxley equations, these findings are well suited for comparison with their biological correlates
  • Keywords
    Hebbian learning; brain models; electroencephalography; neurophysiology; Hebbian synapses; Hodgkin-Huxley equations; autoassociative recall; biologically realistic network; collective network property; mixed pattern input cues; neural activity; neural entrainment; phase-locked bursting patterns; realistic neuron models; reduced order approximation; single neuron model; temporal segmentation; temporal sequence storage; Biological system modeling; Brain modeling; Calcium; Cerebral cortex; Electrodes; Electroencephalography; Equations; Neurons; Robustness; Signal processing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Bioengineering Conference, 1997., Proceedings of the IEEE 1997 23rd Northeast
  • Conference_Location
    Durham, NH
  • Print_ISBN
    0-7803-3848-0
  • Type

    conf

  • DOI
    10.1109/NEBC.1997.594937
  • Filename
    594937