• DocumentCode
    1737867
  • Title

    An episodic memory model using spiking neurons

  • Author

    Berthouze, Luc

  • Author_Institution
    Electrotech. Lab., Tsukuba, Japan
  • Volume
    1
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    86
  • Abstract
    We describe a novel episodic memory model that meets some critical requirements for real-world robotic applications: (a) learn quickly and online, (b) recall patterns in their original order and with preserved timing information and (c) upon cuing, complete sequences from any position even in the presence of ambiguous transitions
  • Keywords
    bioelectric potentials; brain models; neural nets; robots; ambiguous transitions; critical requirements; episodic memory model; preserved timing information; real-world robotic applications; spiking neurons; Feedback loop; Feedforward systems; Firing; Hopfield neural networks; Neural networks; Neurons; Noise figure; Organisms; Robot sensing systems; Timing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man, and Cybernetics, 2000 IEEE International Conference on
  • Conference_Location
    Nashville, TN
  • ISSN
    1062-922X
  • Print_ISBN
    0-7803-6583-6
  • Type

    conf

  • DOI
    10.1109/ICSMC.2000.884969
  • Filename
    884969
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1737867