• DocumentCode
    1903234
  • Title

    A temporal memory network with state-dependent thresholds

  • Author

    Ghosh, Joydeep ; Wang, Shaoyun

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Texas Univ., Austin, TX, USA
  • fYear
    1993
  • fDate
    1993
  • Firstpage
    359
  • Abstract
    A fully connected recurrent network that is capable of storing, recalling, and generating a pattern sequence, is presented. This network reproduces a memorized sequence by synchronous updating, and can independently adjust the duration of occurrence of each pattern in the sequence. Such a capability is obtained by using a state dependent threshold for each cell (which reflects the characteristics of the neuron refractory period), and by the use of the hyperbolic tangent activation function rather than a hard limit. Computer simulations highlight the capabilities of the proposed architecture
  • Keywords
    pattern recognition; recurrent neural nets; temporal logic; fully connected recurrent network; hyperbolic tangent activation function; neuron refractory period; pattern sequence; state-dependent thresholds; synchronous updating; temporal memory network; Associative memory; Computer simulation; Contracts; Delay; Government; Limit-cycles; Neurons; Stochastic processes; Symmetric matrices; Very large scale integration;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1993., IEEE International Conference on
  • Conference_Location
    San Francisco, CA
  • Print_ISBN
    0-7803-0999-5
  • Type

    conf

  • DOI
    10.1109/ICNN.1993.298583
  • Filename
    298583