• DocumentCode
    1756726
  • Title

    Memory Models of Adaptive Behavior

  • Author

    Traversa, Fabio Lorenzo ; Pershin, Yuriy V. ; Di Ventra, Massimiliano

  • Author_Institution
    Dept. of Electron. Eng., Univ. Autonoma de Barcelona, Barcelona, Spain
  • Volume
    24
  • Issue
    9
  • fYear
    2013
  • fDate
    Sept. 2013
  • Firstpage
    1437
  • Lastpage
    1448
  • Abstract
    Adaptive response to varying environment is a common feature of biological organisms. Reproducing such features in electronic systems and circuits is of great importance for a variety of applications. We consider memory models inspired by an intriguing ability of slime molds to both memorize the period of temperature and humidity variations and anticipate the next variations to come, when appropriately trained. Effective circuit models of such behavior are designed using: 1) a set of LC contours with memristive damping and 2) a single memcapacitive system-based adaptive contour with memristive damping. We consider these two approaches in detail by comparing their results and predictions. Finally, possible biological experiments that would discriminate between the models are discussed. In this paper, we also introduce an effective description of certain memory circuit elements.
  • Keywords
    LC circuits; biocomputing; damping; humidity; memristors; LC contours; adaptive response behavior; biological experiments; biological organisms; circuit models; electronic systems; humidity variations; memcapacitive system-based adaptive contour; memory circuit elements; memory models; memristive damping; slime molds; temperature variations; Adaptive frequency; amoeba; dynamical systems; learning; memcapacitive system; memory; memristor; synchronization;
  • fLanguage
    English
  • Journal_Title
    Neural Networks and Learning Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    2162-237X
  • Type

    jour

  • DOI
    10.1109/TNNLS.2013.2261545
  • Filename
    6525332